Article

A Reference Model for Learning Analytics

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Abstract

Recently, there is an increasing interest in learning analytics in Technology-Enhanced Learning TEL. Generally, learning analytics deals with the development of methods that harness educational datasets to support the learning process. Learning analytics LA is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics and visualisation. LA is also a field in which several related areas of research in TEL converge. These include academic analytics, action analytics and educational data mining. In this paper, we investigate the connections between LA and these related fields. We describe a reference model for LA based on four dimensions, namely data and environments what?, stakeholders who?, objectives why? and methods how?. We then review recent publications on LA and its related fields and map them to the four dimensions of the reference model. Furthermore, we identify various challenges and research opportunities in the area of LA in relation to each dimension.

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... A visualização de dados educacionais compreende uma das formas mais efetivas de aplicação da Learning Analytics, uma vez que propõe o uso de ferramentas interativas para o uso no meio acadêmico, transmitindo as informações acerca dos estudantes de forma muito mais dinâmica e intuitiva [Silva et al., 2016;Chatti et al., 2013]. ...
... Tanto para estudantes quanto para professores, é útil ter uma visão geral do andamento de atividades e de como isso se relaciona com o processo educacional [Duval, 2011]. Chatti et al. (2013) destacaram que relatórios com dados estatísticos e tabelas de dados, nem sempre são muito fáceis de serem interpretados. Porém, a implementação dos métodos da LA em uma ferramenta visual, pode facilitar muito a análise de dados educacionais, por parte dos usuários, tornando o processo mais intuitivo. ...
... Para os professores, podem ser úteis para acompanhamento das turmas em tempo real, permitindo a identificação de padrões positivos e negativos, que podem indicar falhas e acertos nos métodos pedagógicos utilizados. Além disso, pode auxiliar na identificação de estudantes de alto rendimento, ou em situação de risco, possibilitando que sejam feitas intervenções a tempo [Chatti, 2013]. ...
Article
Este estudo apresenta o desenvolvimento de um dashboard para ser incorporado a um ambiente virtual de aprendizagem. O dashboard permite aos professores um maior acompanhamento das atividades dos estudantes no ambiente. As diretrizes da pesquisa foram definidas a partir da análise de três outras propostas similares, sendo o método Design Science Research escolhido para o percurso metodológico. Os resultados apontam que o dashboard obteve bons indicadores, tanto para questões de usabilidade quanto para aceitação do artefato proposto.
... There are several strategies that a researcher can use to make a theoretical contribution. The following list presents some strategies outlined by Jaccard and Jacoby (2020, p. [37][38][39][40][41][42][43][44][45] and how I connect the strategies with the contributions of my dissertation: ...
... Learning analytics has roots in applied disciplines of machine learning, intelligent tutoring systems, and data mining (Rosé, 2018). According to Chatti et al. (2012) ics methods include statistics, information visualization, data mining, and social network analysis. ...
... In general, student self-regulation is an essential aim of learning analytics, and institutions should actively enable and encourage students to reflect on their learning and the related data (Greller and Drachsler, 2012). Students and teachers can benefit from learning analytics by self-reflecting on the effectiveness of their learning or teaching practices (Chatti et al., 2012). The visualization of student agency analytics results can be considered, what Baker (2010) calls the distillation of data for human judgment. ...
Thesis
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Pedagogically meaningful, research-based, and ethical learning analytics could foster the values and learning aims we want to advance in our society and educational system. However, it is essential to combine knowledge of the learning sciences and computational sciences when developing and applying learning analytics. This dissertation advances an analytics approach called student agency analytics that utilizes learning analytics methods and computational psychometrics. Student agency is a vital characteristic of a learner, especially during times of uncertainty and change. Student agency has been raised to an important position in educational policymaking, and it has been identified as an essential aspect to consider when facilitating lifelong learning. The research advances the analysis process, examines the results from the student and teacher point of view, and provides novel insights into student agency. Specifically, the research addresses the issue of how to combine theoretical knowledge of learning and analytical methods as a comprehensive process in learning analytics while taking into account teachers’ perspectives, methodological issues, and some limitations in learning analytics. The results show that i) student agency can be characterized, and different profiles can be generated using robust clustering, ii) higher course satisfaction and performance are associated with higher student agency, iii) students reporting low agentic resources experience various restrictive aspects in learning, iv) explainable artificial intelligence techniques can provide additional insight about the intricacies of student agency, and v) teachers can utilize the analytics results in professional reflection and pedagogical decision-making. Available online: https://jyx.jyu.fi/handle/123456789/80877
... In their reference model, Chatti et al. (2012) identified four critical dimensions of LA that need to be considered in its applications: data gathered, managed and analysed (what), target audience (who), objectives (why) and methods (how). The use of LA can pursue several purposes, such as monitoring learning environments, predicting knowledge levels and behaviors, implementing intelligent webbased educational systems, giving automatic and personalized feedback, and (self-)reflecting on the efficacy of teaching practice. ...
... To answer RQ1, the description categories were then classified to TPD, in particular concerning school order of teachers, level of training (Formal, Non-Formal, Informal), educational technology used, duration and period of training. In addition, each paper was coded deductively using relevant LA references: The classification of LA objectives (Chatti et al., 2012), computational approaches (Hoppe, 2017), and data sources from the literature review of Ruiz-Calleja et al. (2017). Definitions of the deductive categories and their coding labels can be found in Figure 1. ...
... Classifying the LA purposes by referring to theChatti et al. (2012) model, it was possible to interpret and codify the results of the 31 selected papers to address the first research question. The most frequent LA purpose is related to monitoring and analysis (n=12). ...
Article
Full-text available
Learning analytics (LA) allows aggregate data about users' to support decision making. Although teachers are recognised as important stakeholders, little is yet explored about the role that LA can play in teacher professional development (TPD). This paper aimed to conduct a systematic review of the use of LA in TPD context, focusing specifically on intervention studies, classifying purposes and methods as well as beneficiaries' engagement and lessons learned. Search terms identified 189 papers and 31 studies were selected based on the inclusion criteria. The results show that most studies adopted data-driven approaches to monitoring teacher behaviours, through automatic extraction of logs in technology-enhanced learning environments. The perspectives, benefits and limitations in the application of LA to TPD are finally presented.
... Learning Analytics (LA) emerged to explore and provide insight from the data in an educational context due to the various levels of granularity of the collected data. LA aims to monitor learners' progress, predict their performance, dropout/retention rates, provide feedback to the learners, provide advice, and facilitate the self-regulation of online learners (Chatti et al., 2012;Papamitsiou & Economides, 2014). However, analytics alone are not enough to improve learning processes (Wong et al., 2019). ...
... However, human intervention is also required to improve the learner's system interactions or level of academic performance. LA implementation is a prerequisite to designing these interventions (Chatti et al., 2012;Clow, 2013;Omedes, 2018). ...
... When starting LA, it is also necessary to draw the boundaries ("what purpose," "for whom," "what data," and "how to analyze") and to reveal objectives due to the broad scope of LA (Chatti et al., 2012). In this context, it is remarkable that by focusing on academic success, most researchers predict performance with LMS data (Conijn et al., 2017;Iglesias-Pradas et al., 2015;Mwalumbwe & Mtebe, 2017;Saqr et al., 2017;Strang, 2016;Zacharis, 2015), compare various techniques to increase the predictive power (Cui et al., 2020;Hung et al., 2019;Miranda & Vegliante, 2019;You, 2016), and predict using individual characteristics and LMS data (Ramirez-Arellano et al., 2019;Strang, 2017). ...
Article
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This study aims to determine indicators that affect students' final performance in an online learning environment using predictive learning analytics in an ICT course and Turkey context. The study takes place within a large state university in an online computer literacy course (14 weeks in one semester) delivered to freshmen students (n = 1209). The researcher gathered data from Moodle engagement analytics (time spent in course, number of clicks, exam, content, discussion), assessment grades (pre-test for prior knowledge, final grade), and various scales (technical skills and "motivation and attitude" dimensions of the readiness, and self-regulated learning skills). Data analysis used multi regression and classification. Multiple regression showed that prior knowledge and technical skills predict the final performance in the context of the course (ICT 101). According to the best probability, the Decision Tree algorithm classified 67.8% of the high final performance based on learners' characteristics and Moodle engagement analytics. The high level of total system interactions of learners with low-level prior knowledge increases their probability of high performance (from 40.4 to 60.2%). This study discussed the course structure and learning design, appropriate actions to improve performance, and suggestions for future research based on the findings.
... The analysis includes a total of 144 articles published between 2012 and 2019 which report the use of learning analytics for personalisation of learning. It was conducted based on the Learning Analytics Reference Model (Chatti et al., 2013), which covers the dimensions of what (data, contexts and environments), who (stakeholders), why (objectives), and how (methods). It also covers the outcomes and limitations of the relevant learning analytics practices in order to examine the potential future work in this area. ...
... Santo et al. (2016) identified factors such as effective collection of students' contextual and personal data in order to have a better understanding of individuals' learning needs. A similar point was stressed by Chatti et al., (2013) -learning solutions that applied learning analytics tools could provide students with learning paths to suit their individual needs or recommend learning contents to them based on their preferences. ...
... In this study, the Learning Analytics Reference Model proposed by Chatti et al. (2013) was adopted to categorise the information of learning analytics practices for analysis. This model conceptualises a learning analytics practice from four dimensions -what (kinds of data collected or contexts/environments where data was collected), who (targeted stakeholders), why (objectives for analysis of data) and how (methods for analysis of data). ...
Article
Full-text available
This paper presents an analysis of learning analytics practices which aimed to achieve personalised learning. It addresses the need for a systematic analysis of the increasing amount of practices of learning analytics which are targeted at personalised learning. The paper summarises and highlights the characteristics and trends in relevant learning analytics practices, and illustrates their relationship with personalised learning. The analysis covers 144 related articles published between 2012 and 2019 collected from Scopus. The learning analytics practices were analysed from the dimensions of what (learning context, learning environment, and data collected), who (stakeholder), why (objective of learning analytics, and personalised learning goal), and how (learning analytics method), as well as their outcomes and limitations. The results show the diversified contexts of learning analytics, with the major ones being tertiary education and online learning. The types of data for learning analytics, which have been increasingly collected from online and emerging learning environments, are mainly related to the learning activities, academic performance, educational background and learning outcomes. The most frequent types of learning analytics objectives and personalised learning goals are enhancing learning experience, providing personal recommendations and satisfying personal learning needs. The learning analytics methods have commonly involved the use of statistical tests, classification, clustering and visualisation techniques. The findings also suggest the areas for future work to address the limitations revealed in the practices, such as investigating more cost-effective ways of offering personalised support, and the transforming role of teachers in personalised learning practices.
... A visualização de dados educacionais compreende uma das formas mais efetivas de aplicação da Learning Analytics, uma vez que propõe o uso de ferramentas interativas para o uso no meio acadêmico, transmitindo as informações acerca dos estudantes de forma muito mais dinâmica e intuitiva [Silva et al., 2016;Chatti et al., 2013]. ...
... Tanto para estudantes quanto para professores, é útil ter uma visão geral do andamento de atividades e de como isso se relaciona com o processo educacional [Duval, 2011]. Chatti et al. (2013) destacaram que relatórios com dados estatísticos e tabelas de dados, nem sempre são muito fáceis de serem interpretados. Porém, a implementação dos métodos da LA em uma ferramenta visual, pode facilitar muito a análise de dados educacionais, por parte dos usuários, tornando o processo mais intuitivo. ...
... Para os professores, podem ser úteis para acompanhamento das turmas em tempo real, permitindo a identificação de padrões positivos e negativos, que podem indicar falhas e acertos nos métodos pedagógicos utilizados. Além disso, pode auxiliar na identificação de estudantes de alto rendimento, ou em situação de risco, possibilitando que sejam feitas intervenções a tempo [Chatti, 2013]. ...
Article
Full-text available
Resumo. Este estudo apresenta o desenvolvimento de um dashboard para ser incorporado a um ambiente virtual de aprendizagem. O dashboard permite aos professores um maior acompanhamento das atividades dos estudantes no ambiente. As diretrizes da pesquisa foram definidas a partir da análise de três outras propostas similares, sendo o método Design Science Research escolhido para o percurso metodológico. Os resultados apontam que o dashboard obteve bons indicadores, tanto para questões de usabilidade quanto para aceitação do artefato proposto. Abstract. This study presents the development of a proposed dashboard to be incorporated into a virtual learning environment. The dashboard should allow teachers to better monitor student activities in the learning environment. The research guidelines were defined based on the analysis of three other similar proposals, the Design Science Research method being chosen for the methodological path. The results show that the dashboard obtained good indicators, both for usability issues and for the acceptance of the proposed artifact.
... While LA can be harnessed to compile, analyze and visualize massive amounts of data impractical to handle manually (Ferguson et al., 2016), it can also provide visible insights into the students' cumulative learning processes and competence development as well as ignite purposeful reflection. Similar arguments have also been made by Chatti et al. (2012). Thus, the data in itself does not lead to learning but requires active, intentional reflection on it for learning to occur. ...
... Students were not viewed as a homogenous group needing the same amount of data or the same type of reflection as a basis for their learning and development but rather, the basis depended on an individual student's needs and preferences. Thus, in line with the views of Chatti et al. (2012), it is important to create individualized approaches for presenting LA data that do not cause information overload but build upon students' existing knowledge and practices. ...
Article
Full-text available
Higher education institutions are challenged to develop innovative educational solutions to meet the competence development requirements set by the emerging future. This qualitative case study aims to identify the future competences considered important for higher education students to acquire during their studies and how the development of these competences can be supported with learning analytics. Reflection on these issues is based on three dimensions (subject development, object, and social environment) of future competences. A special emphasis is placed on the views of 19 teaching professionals gathered from group interviews and analyzed through a qualitative content analysis. The findings indicate that subject development-related future competences, such as reflective competence, self-awareness and self-management, learning literacy, and personal agency and self-efficacy were strongly identified as necessary future competences. The potential of learning analytics to support their development was also widely recognized as it provides means to reflect on learning and competence development and increase one’s self-awareness of strengths and weaknesses. In addition, learning analytics was considered to promote goal-orientation, metacognition and learning to learn, active engagement as well as learning confidence. To deal with complex topics and tasks, students should also acquire object-related competences, such as changeability and digital competence. In addition, they need cooperation and communication competence as well as a developmental mindset to operate successfully in social environments. The use of learning analytics to support most of these object and social environment-related competences was considered promising as it enables the wide exploitation of digital tools and systems, the analysis and visualization of social interactions, and the formation of purposeful learning groups and communal development practices. However, concrete ways of applying learning analytics were largely unacknowledged. This study provides useful insights on the relationship of important future competences and learning analytics while expanding on previous research and conceptual modelling. The findings support professionals working at higher education institutions in facilitating successful conditions for the development of future competences and in advancing purposeful use of learning analytics.
... The next target is Gigabit Networking Yes; it can transfer data on the internet very quickly. [21]. Two surveillance studies on the Internet only and Compare mixed internet / face to face courses. ...
... K Ram Chandra.et.al /Contemporaneity of Language and Literature in the Robotized Millennium, 4(1), 2022,[19][20][21][22][23][24][25][26][27] ...
Chapter
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Web-based learning there is often referred to as online learning or e-learning; Because of this online course content Includes. Debating forums are all possible through the Internet Email, video conferencing and live lectures via (video streaming). Internet Basic Courses Fixed as printed course materials Pages can also be provided. Using the Internet to access course material one of the values is that Web Pages Hyperlinks to other areas of May contain Internet so that a wide range of web-based information can be accessed.A "virtual" learning environment (VLE) or managed learning environment (MLE) refers to teaching and learning software. Is the set. Typically a VLE is a forum for discussion boards, chat rooms, online rating, and Integrates functions such as monitoring student usage and course administration on the Internet. VLEs function as other learning environments in which they distribute information to learners. For example, VLEs help learners collaborate on projects and share information. However, the focus of web-based courses should always be on the learner-technology is not the issue, it is not the answer.
... [Chatti et al, 2012] Colloque 619 -Vers une nouvelle ingénierie pédagogique pour les environnements numériques d'apprentissageCycle de l'analytique d'apprentissage [Chatti et al, 2012] Colloque 619 -Vers une nouvelle ingénierie pédagogique pour les environnements numériques d'apprentissage ...
... Cycle de l'analytique d'apprentissage [Chatti et al, 2012] Colloque 619 -Vers une nouvelle ingénierie pédagogique pour les environnements numériques d'apprentissage ...
Article
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Les dispositifs numériques ont colonisé de nombreuses facettes de notre quotidien et l’école obligatoire s’approprie maintenant cette évolution. Les finalités annoncées sont souvent associées à la notion de citoyenneté numérique. Parallèlement, les institutions scolaires définissent les environnements numériques d’apprentissage que les enseignants et élèves doivent adopter. Se positionnant dans les humanités numériques, les auteurs interrogent les définitions affichées de la citoyenneté numérique, les confrontent à la nature sui generis du numérique et de son industrie, pour enfin questionner le rapprochement entre ceux-ci et l’école publique. Des enjeux d’émancipation citoyenne, d’autonomie et de gouvernance servent l’analyse et permettent de conclure à la nécessité de débattre les contradictions pédago-numériques.
... For example, recorded log data is potential data for event learning analytics with timestamps about viewing certain resources, completing essays and quizzes, or discussion messages viewed or sent . Learning analytics is considered an interdisciplinary field within the fields of educational technology, pedagogy, machine learning, business intelligence, artificial intelligence, and statistics as a new field of study (Guenaga & Garaizar, 2016;Siemens, 2013, Chatti et al., 2012. The aim of learning analytics is to improve learning, teaching, and learning environment by using educational data (Clow, 2013). ...
Conference Paper
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Learning analytics provide valuable information for learners and instructors by combining and analyzing learners' historical data during the learning experience. The most common way of employing this information is in the form of learning analytics dashboards (LADs). This study primarily aims to propose LADs design based on the perspectives of various stakeholders. The secondary aim of the study is to propose the concept of 'learning analytics feedforward'. After an iterative and formative design process, the LADs were developed in two different interfaces: a course-related dashboard and a topic-related dashboard. Each dashboard element is classified according to whether it contains feedback or feedforward. The development of LADs based on learner expectations and lecturer perspectives is described in detail.
... In ihrer bisherigen Darstellung enthalten die Muster jedoch kein interdisziplinäres Gestaltungswissen und können Entwickler nicht bei interdisziplinären Problemstellungen unterstützen [8] [4]. Ein wichtiges Merkmal hierbei ist die individuelle Adaption an den Lernenden und seinen aktuellen Lernstand [9]. Die individuelle Anpassungsfähigkeit des Lernassistenten an den Nutzer ermöglicht es, sowohl schnell, als auch individuell Feedback zu geben [10]. ...
Chapter
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Zusammenfassung Durch die Digitalisierung werden immer mehr Technologien entwickelt. Dabei gewinnt die soziotechnische Systementwicklung zunehmend an Bedeutung, in deren Rahmen nicht nur das technische System isoliert betrachtet wird, sondern auch der Nutzer und sein Umfeld. Insbesondere bei der Entwicklung rechtsverträglicher Systeme stehen Entwickler häufig aufgrund fehlenden rechtlichen Fachwissens vor großen Herausforderungen. Dies gilt insbesondere dann, wenn es um intelligente, selbstlernende Systeme geht. Diese Systeme sammeln, um die Qualität ihrer Dienste zu optimieren und Nutzerbedürfnissen zu entsprechen mithilfe leistungsfähiger Technologien große Mengen an personenbezogenen Daten, was Risiken für die informationelle Selbstbestimmung der Nutzer mit sich bringt. Um diesen Risiken entgegenzuwirken nutzen wir Anforderungs- und Entwurfsmuster. Ziel des Beitrags ist daher mittels eines multi-methodischen Ansatzes aufzuzeigen, welchen Beitrag interdisziplinäre Anforderungs- und Entwurfsmuster für die Entwicklung rechtsverträglicher und qualitativ hochwertiger KI-basierter Systeme leisten können. Um die Wirksamkeit der Muster zu untersuchen wurde mithilfe der Muster ein Lernassistent entwickelt und durch die Methode der Simulationsstudie evaluiert.
... Il concetto di LA affonda le sue radici in settori disciplinari diversi (Chatti, Dyckhoff, Schroeder, & Thus, 2012) ed utilizza tecniche, metodi di raccolta dei dati e strumenti, sviluppati in ambiti come la Business Intelligence, il Web Analytics, i Recommender Systems (Persico, 2015) e l'Educational Data Mining (Romero & Ventura, 2007), al fine di gettare luce sulla nebbia di incertezza che avvolge il futuro dell'istruzione superiore, e rendere più evidente come allocare le risorse, come sviluppare vantaggi competitivi e, soprattutto, come migliorare la qualità e il valore dell'esperienza di apprendimento (Long & Siemens, 2014). ...
Article
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The Covid-19 affected people regardless of nationality, level of education, income or gender. However, students from privileged backgrounds, supported by their parents could find their way past closed school doors to alternative learning opportunities. This crisis has exposed the many inadequacies and inequalities in our education systems. This article presents the GPU System as a tool for collecting, managing and monitoring. The PON 2014/2020 For the School has been conceived for achieving an intelligent, equal, sustainable, and inclusive growth. In order to measure the learnings performance of students, a probability model was implemented to measure performance improvement. The data refer to the grades attributed to students before and after the delivery of the educational activities. Results show that the probability of registering a training success triggered by the training course is greater for the foreign languages area, generating inclusion and social integration mechanisms, as well as mediation and intercultural understanding. Il sistema di istruzione e di formazione dopo il Covid-19: risultati da un modello per misurare gli apprendimenti degli studenti. Il Covid-19 ha colpito tutti gli individui indipendentemente dalla nazionalità, dal livello di istruzione, dal reddito o dal genere. Tuttavia, gli studenti provenienti da ambienti privilegiati, supportati dai loro genitori hanno potuto intravedere più agevolmente la loro strada oltre le porte chiuse della scuola verso opportunità di apprendimento alternative. Questa crisi ha messo in luce le molte inadeguatezze e disuguaglianze nei nostri sistemi educativi. In questo studio si presenta il Sistema GPU come strumento di raccolta, gestione e monitoraggio. In tale contesto si inserisce il PON 2014/2020 Per la Scuola, concepito per realizzare una crescita intelligente, equa, sostenibile e inclusiva. Al fine di misurare le performance degli apprendimenti degli studenti è stato implementato un modello di probabilità finalizzato a misurare il successo formativo. I dati si riferiscono alle votazioni attribuite agli studenti prima e dopo l’azione formativa. I risultati mostrano come la probabilità di registrare un successo formativo generato dal percorso formativo intrapreso sia maggiore per l’area relativa alle lingue straniere, generando meccanismi di inclusione ed integrazione sociale, nonché la mediazione e la comprensione interculturale.
... Paso de la innovación a la investigación educativa en las ingenierías Modelo de referencia de Chatti [80] Paso de la innovación a la investigación educativa en las ingenierías 46 ...
Presentation
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Conferencia invitada en las Jornadas Docentes 2022 “La investigación en la educación de ingeniería”, organizadas por la Universidad Andrés Bello (Santiago de Chile) el 24 de enero de 2022. En esta conferencia se propone establecer sinergias entre la innovación y la investigación educativas, con especial atención al campo de las ingenierías.
... Damit Studierende Feedback durch ein LA Tool erhalten können, müssen diese aktiv am LA Prozess teilnehmen. Dies beinhaltet unter anderem das Sammeln von Lerndaten, die Analyse dieser Daten und die Repräsentation der Analyseergebnisse mit der Ableitung von nächsten Lernschritten durch die Studierenden [Ch12]. ...
Conference Paper
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In diesem Beitrag werden die Ergebnisse des Workshops zur Ermittlung von Anreizsystemen für Studierendenpartizipation in Learning Analytics vorgestellt. Ziel des Workshops war es eine Antwort auf die Frage zu finden, wie Learning Analytics für Studierende attraktiv gemacht werden kann. Dazu wurden Erfahrungen und Ideen zu fünf Unterfragen über die Interessen der Studierenden, die Kommunikation der Vorteile von Learning Analytics, dem Datenschutz, der Notwendigkeit von personalisierten Daten und wie Studierenden schon mit wenig gesammelten Daten hilfreiches Feedback gegeben werden kann, gesammelt. Daraus abgeleitet wurde festgehalten, dass ein Learning Analytics Tool, welches die Studierenden gut im Studium unterstützt, selbst den größten Anreiz darstellen kann. Um eine erste Nutzergruppe aufzubauen, welche das Tool später weiterempfehlen könnte, wurden Wege der Kommunikation der Vorteile und des Mehrwerts sowie die Einhaltung der Datenschutzregularien diskutiert und hier beschrieben.
... In the second step, we used the learning analytics reference model (LARM) put forward by Chatti et al. (2012) to perform a descriptive analysis of the selected literature. The LARM includes four elements: data (what?), stakeholders (who?), objectives (why?), and methods (how?). ...
Article
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Wearable devices are an emerging technological tool in the field of learning analytics. With the help of wearable technologies, an increasing number of scholars have a strong interest in studying the associations between student data and learning outcomes in different learning environments. This systematic review examines 120 articles published between 2011 and 2021, exploring current research on learning analytics based on wearable devices in detail from both descriptive and content analysis. The descriptive analysis reviewed the included literature in five dimensions: publication times of the reviewed literature, wearable devices and data types used in studies, stakeholders, objectives, and methods involved in the analysis procedure. The content analysis aims to examine the literature covered in terms of three categorical domains of educational objectives: cognitive, affective, and behavioral, to investigate the practical applications and potential issues of learning analytics based on wearable devices. After that, based on the overall research content of the reviewed literature, a framework for learning analytics based on wearable devices is present, and its application process is summarized and analyzed for the reference of related researchers. At last, we summarize the limitations of existing studies and present several recommendations to further promote research and development in this field.
... The categorization performed here is based on the work of [13], which evolves from a model originally proposed by [19], adapted by [20] and [21]. The multidimensional model consists of the following seven dimensions: 1) What (data sources), 2) Why (goals of the analysis), 3) How (techniques employed), 4) Who (stakeholders), 5) Maturity level, 6) Viberg's research approach [22], and 7) Ethical issues. ...
Conference Paper
Learning Analytics Dashboards are useful tools for presenting data visually. This work presents a systematic mapping study of publications from 2010 to 2020 (May) about Learning Analytics and visualization tools for Moodle. A total of 238 papers were collected from Scopus, Web of Science, and CAPES Portal. From those, 51 were selected to be analyzed according to a seven dimension model involving the following criteria: data sources; goals of analysis; stakeholders; research approach; maturity of the tool; ethical issues; and specificities of the tool. The analysis showed that most of the tools are developed to be used by the teachers, and that the most frequent goal of the tools is to allow the comparison of students' metrics. Moreover, the analysis also showed a huge amount of papers proposing (still not developed) tools focused on prediction and intervention, thus indicating a growing interest in this topic. At last, it was also observed a lack of information available regarding ethical issues over the collection, processing and storage of personal information by the existing LAD.
... For the adoption of LA, institutions should know their objectives. By far, LA has been adopted most commonly to achieve the following objectives (Chatti et al., 2013): monitoring and analysis, prediction and intervention, tutoring and mentoring, assessment and feedback, adaptation, personalization and recommendation, or reflection. In addition, different LA tools and services can be designed and implemented to achieve these objectives. ...
... Esse campo pode envolver procedimentos de mineração de dados e de learning analytics. Nesse sentido, as instituições educacionais podem se beneficiar de técnicas de learning analytics para apoiar a tomada de decisão, identificar potenciais alunos "em risco", melhorar o sucesso do aluno (ou seja, taxas de retenção e formação), desenvolver políticas de recrutamento de alunos e ajustar o planejamento do curso, entre outras questões (Chatti et al., 2012). ...
... Además, identificaron varios desafíos y oportunidades de investigación en el área de AL en relación con cada dimensión. [3] Por su parte Doug Clow desarrolló un artículo aplicando el modelo de cinco pasos de Campbell y Oblinger [4] de análisis de aprendizaje (captura, informe, predicción, actuación, refinamiento) y otras teorizaciones del campo, y se basa en una teoría educativa más amplia para articular una Ciclo de análisis de aprendizaje más desarrollado, explícito y basado en la teoría. Este ciclo conceptualiza el trabajo exitoso de análisis de aprendizaje como cuatro pasos vinculados: los alumnos (1) generan datos (2) que se utilizan para producir métricas, análisis o visualizaciones (3). ...
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La analítica de datos es una rama de la Informática que apoya a la toma de decisiones en diversas áreas del conocimiento, la educación no es la excepción, año tras año los docentes imparten sus unidades de aprendizaje en donde la aplicación de exámenes arroja un sin fin de información que, en el mejor de los casos queda almacenada en el equipo de cómputo que funja como servidor y ofrezca un Sistema para Gestión de Aprendizaje (Learning Management System – LMS por sus siglas en inglés) como puede ser Moodle, y queda sin utilizarse. El aplicar técnicas de análisis de datos a esta información se puede ponderar el grado de avance en cuanto al dominio de una habilidad en Tecnologías de la Información y la Computación (TIC), como pueden ser las certificaciones que otorga la empresa CISCO®, hoy sabemos que las certificaciones se han vuelto indispensables para lograr una ventaja competitiva entre los profesionistas de las TIC y dar una oportunidad para posicionarse en un buen empleo en un mundo globalizado. El presente trabajo de investigación analiza, mediante un estudio de alcance descriptivo, comparativo y correlacional, la unidad de aprendizaje “Comunicación entre Computadoras” de las generaciones 2016 a la 2020 de los grupos de la Licenciatura en Informática Administrativa LIA D1, LIA D2, LIA D3 y LIA D4 del Centro Universitario UAEM Atlacomulco, a través de una minería de datos al sistema de reactivos de exámenes aplicados por el docente ubicados en una plataforma LMS Moodle, para posteriormente codificarlos y compararlos con las certificaciones CISCO® CCNA 100-101 (ICND1), 200-101 (ICND2) y 200-120 (CCNA R & S) para determinar si los discentes tienen las habilidades requeridas para presentarlas y aprobarlas.
... Chatti, Dyckhoff, Schroeder and Thus [Chatti, Dyckhoff, Schroeder & Thus 2012] propose an iterative reference model for LA involving data collection and pre-processing, analytics and action and post-processing based on four dimensions: What kind of data does the system gather, manage, and use for the analysis? Why does the system analyse the collected data? ...
Conference Paper
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While learning analytics frameworks precede the official launch of learning analytics in 2011, there has been a proliferation of learning analytics frameworks since. This systematic review of learning analytics frameworks between 2011 and 2021 in three databases resulted in an initial corpus of 268 articles and conference proceeding papers based on the occurrence of "learning analytics" and "framework" in titles, keywords and abstracts. The final corpus of 46 frameworks were analysed using a coding scheme derived from purposefully selected learning analytics frameworks. The results found that learning analytics frameworks share a number of elements and characteristics such as source, development and application focus, a form of representation, data sources and types, focus and context. Less than half of the frameworks consider student data privacy and ethics. Finally, while design and process elements of these frameworks may be transferable and scalable to other contexts, users in different contexts will be best-placed to determine their transferability/scalability.
... A review of the Learning Analytics field (Chatti et al, 2012), for instance, categorized different applications of digital learner data: 1. monitoring and analysis 2. prediction and interventions 3. assessment and feedback 4. intelligent tutoring and adaptation 5. personalization / recommendation 6. individual reflection. None directly dealt with improvements to curriculum itself. ...
... The science of analysing data to enhance active decision-making has received widespread attention among businesses and educational institutions. Learning Analytics, which is an emerging field, combines mathematical and statistical modelling, data visualise and information systems [23,24]. This provides users with innovative information technologies that will enable them to make actionable decisions. ...
Conference Paper
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Universities across the world use different policies in data collection thus, some have different ways of using Learning Analytics (LA) to assist in improving students' performance. The purpose of this paper was to investigate the perceptions of LA as a tool for enhancing students' performance. 1,395 lecturers in 12 countries, spread through 5 continents participated in the survey picked randomly to ensure diversity in this research. The findings revealed that the majority of the lecturers are aware of LA and their institutions use a variety of LA tools. The study also found that most lecturers use these innovative tools in their institutions. However, there are challenges such as skills development, incompatible institutional policies and lack of resources, which hindered the full implementation and use of LA tools in higher educational institutions.
... Machine learning has its applications ranging from medical engineering to aerospace and is contributing to enhancing and improving the functionality, performance, and accuracy of the systems involved in all disciplines. In academic analytics, researchers are using machine learning to improve the performance and accuracy of their learning management systems and e-learning platforms [37]. Algorithms have been developed and new approaches have been proposed to perform regression, classification, and prediction in LMSs and e-learning portals. ...
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Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.
... • Learning Analytics (LA) : According to a review in [36], there are many goals of using LA, such as, monitoring and analysis, assessment and feedback, adaptation, prediction and intervention, personnalization and recommendation, tutoring and monitoring, etc. ...
Thesis
The Internet of Everything (IoE) is gaining a growing interest in scientific and industrial communities to have expanded the Internet of Things (IoT) paradigm. In fact, several domains have been through a digital transformation evolution, for instance Industry 4.0, however, a lot still needs to be done regarding the educational domain especially at university. Instructional design is a major concern among the TEL (Technology Enhanced Learning) community. The covid-19 health crisis has dug deeper into several of its challenges. The objective of Industry 4.0 is to foster the integration of complex physical machinery and devices based on Cyber Physical Systems (CPS) and IoT. A set of sensors, actuators and software components are used to monitor, analyze, and control smart factories through control loops (MAPE). In this thesis, we investigate how these concepts could contribute in shaping and engineering the next revolution at university (University 4.0). To do so, we have conducted an exploratory approach in order to, first give a definition of what we call Educational Cyber Physical Systems (ECPS) to build it upon ideas from the literature. This concept is barely addressed in the TELE domain. We apply this concept and its utilization in different educational levels (Classroom, Course, Curriculum). Then, we propose a model-driven method (ModelECPS) to design these systems founded on a modeling process and three Domain Specific Languages (DSL). The modeling process is issued from the Object Management Group (OMG) standards that define the Y process based on three modeling pillars of the MDE method. The platform Independent Model (PIM) represents the educational meta-model, called EML4.0. The Platform Definition Model (PDM) is the connected environment meta-model, called CPSML, that extends the modeling “things” into modeling “everything”. Finally, the ECPSML defines the meta-model that describes the educational connected environment and thus the Platform Specific Model (PSM). Its design is based on concept alignment between both PDM and PIM models and is semi-automatically generated through applying ATL rules (Model-to-Model transformation). A utilization modeling process for the method is given in order to guide the different stakeholders in designing such systems (ECPS). To illustrate our proposal, we conducted a case study on a real-life ECPS classroom during the covid-19 health crisis. We describe the required models designed in this process and supervise students' progress during the practical sessions.
... McCulloch [21], was a representation of neural networks. The applications of machine learning have evolved significantly, many learning analytics applications utilise machine learning in order to gain more information from educational data [22]. S. Kotsiantis et al. used machine learning to predict student performance in distance learning [23]. ...
Thesis
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Learning analytics is a rapidly growing field within the area of data science, having the ability to improve student outcomes by optimising learning environments. This piece of work aims to examine the links between course structure and student performance, so that these results can be reported back to educators and used to make informed decisions about learning environments for students. This is done following a data science process, using feature selection to narrow down the relevant subsets of features in order to improve the predictive power of the machine learning models. The results from this work show that there is a weak link between total section counts and the evaluation class metric (class variable for student performance). This weak link is derived from its ability to increase the predictive power of a model (highest observe increase in accuracy = 9.77%), furthermore, the total section counts were part of the subsets of features which provided the highest accuracy and F-Measure results during the classification task. Many different subsets of features were tested to find a model with the highest predictive power, and the results showed that the initial data-set provided the best predictive power (65.51% accuracy and 0.640 overall F-Measure). These results told us that this data-set was unable to provide a subset of features which would give sufficient evidence to link course structure with student performance.
... Schools need to make significant investments into data infrastructure to enable data collection, storage, and use. Although technical dimensions are often highlighted (Kitto et al., 2020), data infrastructure has equally important social dimensions with implications for relevant stakeholders (Chatti et al., 2012;Siemens et al., 2013). Data infrastructure has implications on 'invisible' workloads of teachers who are to perform additional technical and secretarial tasks, often insufficiently appreciated, to produce and assure completeness and quality of data. ...
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Educational systems generate huge quantities of digital data. Digital educational data is captured and used at all points -- from classrooms and schools, to the level of educational departments. As growing trends in ‘data-driven instruction’ suggest, all these data have great potential to support student, teacher and leadership practices, help guide work and learning decisions, and inform policy development. Moreover, an increasing focus is being placed on the development of artificial intelligence to automate and improve how data are used. Yet, stakeholder data practices remain invisible and little understood, which complicates how artificial intelligence can be embedded in this context. In this paper, we introduce an educational data journeys framework to frame dynamics of data power, data work, identities and literacies. This approach is employed to explore educational policy reveal data flows and frictions in school improvement and what this may imply for the development of artificial intelligence in education.
... The roots of designing MOLAM were influenced by Khalil & Ebner's Learning Analytics Lifecycle model (2015) and Park's Pedagogical Framework for Mobile Learning (2011) which both build upon Zimmerman's (1990) SRL model and Winne's (2017) grain size explanation of learning analytics for SRL. The Learning Analytics Lifecycle (Khalil & Ebner, 2015) was adapted since it refines three of the early developed learning analytics models (Chatti et al., 2013;Clow, 2012;Greller & Drachsler, 2012) as well as providing a solid foundation for the purposes of employing learning analytics with SRL. Park's (2011) model for m-learning delivers a sound theoretical framework to build mobile applications for the purpose of 'learning-onthe-go'. ...
Chapter
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Online distance learning is highly learner-centred, requiring different skills and competences from learners, as well as alternative approaches for instructional design, student support, and provision of resources. Learner autonomy and self-regulated learning (SRL) in online learning settings are considered key success factors that predict student performance. Research suggests that learners may struggle in online, open, and mobile learning environments when they do not use critical SRL strategies. This chapter argues that the effective use of SRL would be beneficial in these contexts, although this can be difficult for both learners and educators, particularly when students are learning online and/or independently. The chapter introduces a Mobile Multimodal Learning Analytics approach (MOLAM) aimed at guiding learners, teachers and researchers wanting to develop, successfully employ and/or evaluate learning analytics approaches for mobile learning activities for the purposes of measuring and fostering student SRL in diverse online learning environments. MOLAM is especially valuable for continuous measurement and interventions, thus fostering students’ transferable SRL skills, strategies, and knowledge across formal, informal, and non-formal online learning settings. The chapter concludes suggesting that mobile multimodal learning analytics should be performed with careful integration of relevant support mechanisms and frameworks to protect student privacy and ensure their agency.
... Before using Learning Analytics, one should understand its what, who, why, & how, essentially. What: The big data, data sources, context, and learning environment; Why: Targeted stakeholders (students, educators, educational institutions, researchers, course designers, and developers); Why: Purpose of data analysis (Evaluation of teaching-learning, intervening, predicting patterns and trends, assessing, feedback, customization (Chatti, 2012a;2012b), recommendations, suggestions, creating awareness, promoting reflection, etc.); How: Methods and tools for data analysis (statistical tools, visualization, data mining, social networking, etc.). This work contributes to Learning Analytics research by providing a more grounded understanding of it and related ideas and identifying strengths, weaknesses, opportunities, and challenges (SWOC) in this developing subject that has STRENGTHS 1. Learning analytics provides teachers and trainers with key information based on student data. ...
Article
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Many parts of our lives are influenced by data science. It has a tremendous influence on the corporate world, healthcare, and other fields. The field of education is no exception. Educational big data is a new discipline connecting scholars and practitioners from various domains to comprehend and enhance learning processes using information-driven solutions. Learning analytics is a relatively recent subject in the field. It looks at different ways to use big data, machine learning/deep learning, computational linguistics, visualizations/graphics, man-to-machine communication techniques, etc., to give teachers and learners insights on improving teaching-learning activities. In the light of the afore-mentioned observations, the present research is intended to achieve dual objectives. Initially, the research paper attempts to contextualize and provide a detailed description of learning analytics and its tools from a broader perspective by focusing upon different Learning Management Systems (LMS) such as Google Classroom, Blackboard, Moodle, Canvas, etc., and different tools or plugins utilized by them to analyze and report educational data. Subsequently, the paper includes a SWOC analysis conducted by the authors to understand the strengths, weaknesses, opportunities, and challenges associated with Learning Analytics.
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Les handicapés, même s’ils sont scolarisés, ont souvent une probabilité bien supérieure d’abandonner les études et de quitter l’école prématurément. Pour les enfants handicapés qui réussissent à entrer à l’école, il existe un risque de ne pas garantir la qualité et la forme de la scolarisation qu’ils reçoivent. En vue de mettre en œuvre ce droit sans discrimination et sur la base de l’égalité des chances, les États doivent assurer un système d’éducation inclusive à tous les niveaux. Cela signifie que les États sont obligés de s’assurer que les personnes en situation de handicap ne sont pas exclues du système d’éducation général à cause de leur handicap.
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Enseignement personnalisé et inclusif sur les MOOCs basé sur une analyse des traces d'évaluation chebbi imen , chabbiimen@yahoo.fr Résume : La personnalisation de l'apprentissage représente un des domaines de recherche les plus étudiés dans le cadre des EIAH. Avec l'apparition des MOOC et surtout de leur caractère massif et ouvert, la question de la personnalisation prend de plus en plus d'ampleur. L'objectif de ce sujet de thèse est de proposer un Framework de personnalisation sur les MOOCs , prenant les différents profils d'apprenants et incluant les apprenants avec un handicap, en se basant sur une analyse des traces d'évaluation. L'idée est d'obtenir une infrastructure combinant à la fois une vue personnelle et une vue communautaire pour l'utilisation des connaissances. Cette infrastructure devra comprendre des mécanismes permettant aux utilisateurs d'évoluer facilement dans l'environnement d'apprentissage en fonction de leurs niveaux de connaissances et l'inclusion des personnes handicapées dans le processus d'apprentissage en ligne et pour offrir un environnement d'apprentissage plus accessible avec la possibilité de personnaliser le chemin et les Contenus d'apprentissage pour mieux répondre aux besoins spécifiques de l'apprenant.
Chapter
Learning analytics aims at investigating learning processes. This allows researchers to advance educational research, and it is sometimes used to provide instructors feedback to enhance teaching. Prior research suggests that learning analytics can be used to support students, for instance, by providing individualized feedback, which enables them to reflect on and improve their learning. In practice, this learner-centered view on learning analytics is, however, overshadowed by research and instructor-centered interests, particularly in formal learning settings. Due to this lack of empirical experience from field studies of providing students access to their own learning data, students’ interaction with learning analytics is still an open research area. We show that students in large-scale formal educational settings welcome the opportunity to access their own data using learning analytics dashboards and particularly are interested in getting feedback on self-regulated formative assessments. We investigate the students’ interaction with a learning analytics dashboard in a field study on two introductory courses. The collected usage data reveal how approx. 800 students actively engaged with the dashboard. We found that students are interested in getting aggregated overview information on their and fellow students learning processes and success but are also interested in diving deep into particular learning aspects. Our results show that learner-oriented learning analytics dashboards are a suitable tool to give students access to their own data. Dashboard implementations should provide (a) comparative overviews on students’ learning as well as (b) the possibility to dive deep into specific analyses that are of individual interest.
Chapter
Student-facing dashboards allow learners to monitor their own processes and make decisions about their learning, based on their own interaction data. While most learning analytics dashboard studies focus on how to collect and analyze data, optimal design and visualization of the feedback given through LADs and closing the feedback loop remain undefined. This study aims to put forward design suggestions for effective student-facing learning analytics dashboards, with emphasis on feedback, visualization, and gamification. Effective feedback design emphasizes presenting the gap and giving recommendations on how to close it, which is the precursor to powerful dashboard design. Gamification is included in the dashboard design to motivate learners to take action by considering the feedback and the potential rewards for their actions. Compelling information design directs learners through visualization of their feedback, gamification components, and the layout of the dashboard. In the process of visualizing an effective dashboard, it is essential to consider the literacy differences of learners while clearly establishing their status, their recommendations, and their learning process relevant to the class performance as a whole. This study is completed by introducing a hypothetical dashboard that is designed in accordance with the suggestions given.
Chapter
Learning analytics aim to understand and optimize learning and learning environments by using learner, instructor, and system interaction data. Likewise, a subfield of learning analytics, assessment analytics aim to monitor the learners and learning process, tracking, and recording assessment data, provide feedback, predict the future state of learners, and especially make progress in learning outcomes using especially assessment data. Assessment analytics dashboards enable the visualization of the data obtained from the assessment results and interactions with an assessment task. Thus, it provides learners and instructors to monitor and reflect on their online teaching and learning patterns. The prime purpose of this chapter linking e-assessment results with assessment analytics dashboards. For this purpose, an evidence-centered assessment analytics process model has been proposed and explained in detail. In this chapter, assessment analytics framework, basic e-assessment approaches, measurement theories, sequential pattern analysis, classification algorithms, and the creation of the caution index are presented, respectively. After this, the goals of e-assessment analytics and data visualization options such as graphs and charts that matching those goals are discussed. Consequently, this chapter is expected to guide users in establishing e-assessment analytics process and visualization of the analytics’ results.
Chapter
Assessment analytics are based on students’ interactions with assessment tasks and feedback via dashboards in online learning systems. Learners’ engagement with feedback is especially important in formative assessment processes. This study aims to examine the transitions between types of feedback presented via a student-facing dashboard using the navigation data. Based on this aim, a standalone assessment system was developed for students to test themselves. Following the assessment tasks (tests in this system), four different types of feedback were presented to the learners: (a) criteria-referenced feedback (CRF), (b) elaboration feedback (EF), (c) self-referenced feedback (SRF), and (d) norm-referenced feedback (NRF). Each feedback as an indicator was provided to the learners in different dashboards, and the learners could see each indicator with making transitions between dashboards. Quantitative data was collected from 100 freshmen students, consisting of learners’ interaction with the system. Lag sequential analysis (LSA) was utilized to determine which indicators the learners consecutively visited. According to the findings, students did not confine themselves with just one indicator, and made systematic transitions between each dashboard, CRF, EF, SRF, and NRF respectively. The second research finding was that there was a difference in transitions between the types of feedback in master and non-master students. The findings are discussed in detail below, and recommendations are presented in the relevant sections.
Chapter
There is a pressing need for data management and learning management systems. Educational data mining and learning analytics are two related aspects of educational technology that promote an overall effective teaching-learning system. The news media has the potential to act as a tool of learning analytics since they can easily access information at a mass scale. There are instances of leading newspapers organizing different educational programs where students from all the social layers have an opportunity to participate. A review of the programs reveals that all the programs collect and analyze educational data, which can form a research base of learning analytics. This chapter presents the description of three such educational programs organized by the leading media houses of India. This chapter also reflects on the contribution to learning management systems and educational data mining for the improvement of the overall educational system.
Chapter
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Europäische und deutsche Gesetzgeber:innen und Behörden sind sich einig, dass sie die negativen Folgen von Marktkonzentration begrenzen wollen. Doch inwieweit wirkt sich fehlender Wettbewerb auf den Schutz der Privatsphäre aus? Im Kontext des Facebook-Verfahrens werden v. a. zwei verschiedene Ansätze beleuchtet – einerseits, inwiefern Konzentration den Umfang der gesammelten bzw. genutzten Daten beeinflusst, und andererseits, wie Konzentration sich auf die Verhandlungsposition der Nutzer:innen auswirkt. Dieser Beitrag zeigt auf, dass der erste Ansatz nur spärlich empirisch gestützt ist, während der zweite weiterer konzeptioneller Ausarbeitung bedarf. Im Anschluss wird untersucht, inwieweit nicht nur Marktkonzentration, sondern fehlende Befähigung von Verbraucher:innen dazu führt, dass es keinen wirksamen Wettbewerb um besseren Datenschutz gibt. Beide Teile schließen mit politischen Handlungsempfehlungen ab.
Conference Paper
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Virtual reality can be used to support computer graphics teaching, e.g. by offering the chance to illustrate 3D processes that are difficult to convey. This paper describes the development and first evaluations of RePiX VR a virtual reality tool for computer graphics education, which focuses on the teaching of fundamental concepts of the rendering pipeline and offers researchers the opportunity to study learning in VR by integrating learning analytics. For this, the tool itself is presented and the evaluation, which uses quantitative methods and learning analytics to show the effectiveness of the tool. The first evaluations show that even learners without prior knowledge can use the VR tool and learn the first basics of computer graphics.
Article
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هدفت هذه الدراسة إلى تطوير بيئة التعلُّم الإلكتروني لطلاب كلية الشرطة في أبوظبي في ضوء تحليلات التعلُّم، اعتمد الباحث من أجل ذلك على تصميم وبناء قائمة معايير قياسية لبيئة التعلُّم الإلكتروني روعي فيها طبيعة البرنامج الأكاديمي والتدريبي لطلاب كلية الشرطة، حيث تعتمد أغلب المساقات الدراسية أسلوب الدراسة العملية التطبيقية من خلال استراتيجيات تدريس تجعل الطالب دوماً هو محور العملية التعليمية، وبالتالي أصبحت مخرجات التعلُّم في كل المساقات الدراسية تستهدف قمة هرم بلوم (التطبيق والتحليل والتركيب والتقويم). ولتحقيق أهداف الدراسة والاجابة على أسئلتها، اتّبع الباحث المنهج الوصفي التحليلي، وذلك من خلال مجموعة من الإجراءات تنوعت بين النظري والتطبيقي، حيث قام الباحث بتصميم استطلاع رأي محكّم للطلاب وأعضاء هيئة التدريس، وذلك للوقوف على أوجه القصور في بيئة التعلُّم الإلكتروني القائمة بالفعل، وذلك من أجل العمل على تطويرها في البيئة الجديدة، وتوصلت نتائج الدراسة من خلال تحليل الاستطلاعات وتحليلات التعلُّم إلى وجود قصور في تصميم المحتوى الإلكتروني، حيث أن المحتوى بحاجة إلى تطوير يتناسب مع استراتيجيات التعلُّم التعاوني والتشاركي، بحيث يكون أكثر تفاعلية، ويتم عرضه بأكثر من طريقة تناسب أنماط التعلُّم لدى الطلاب، كما توصّلت نتائج هذه الدراسة من خلال تحليل تقارير نظام إدارة التعلُّم، إلى اقبال الطلاب على الأنشطة التي ترتبط بأساليب التقييم المباشرة مثل التكليفات، أوراق البحث، الاختبارات الإلكترونية، مما يستلزم السعي في تطوير هذه الأنشطة بأحدث الأساليب لتعطي استفادة أكبر للطلاب، يقترح الباحث ضرورة تطوير وتحديث بيئات التعلُّم الإلكتروني وتفعيل أنظمة تحليلات التعلُّم المعتمدة على خوارزميات الذكاء الاصطناعي، مما يساعد في متابعة تقدم الطلاب، والوقوف بصورة دائمة على نقاط القوة والضعف في كل عناصر بيئة التعلُّم الإلكتروني ليس فقط بعد إتمام دراسة المساقات ولكن اثناء الدراسة وهدا هو المهم.
Chapter
A growing number of virtual courses are being offered by Brazilian educational institutions, requiring the development of technological resources and research to assist in Distance Education (DE) teaching and learning processes. Analysis of students’ socio-affective profiles in Virtual Learning Environments (VLE) enables possibilities to develop methodologies and resources to better understand them. The Social Map (SM) and Affective Map (AM), both features of the Cooperative Learning Network (in Portuguese: ROODA), provide inferences and graphic presentations of students’ socio-affective profiles. This article aims to identify the possible recurrent socio-affective scenarios in a VLE utilizing Learning Analytics (LA). LA is defined as the measurement, collection, and analysis of data. The qualitative and quantitative research approach used in this work was carried out based on 10 case studies. The target audience was 219 students including undergraduate, graduate, teachers, and elderly people who participated in teaching activities at a university. Data collected from the SM and AM were extracted in order to identify the relationship between these two aspects. As a result, 38 socio-affective scenarios were created using LA to contribute to the analysis of the students’ learning profiles, allowing teachers to develop pedagogical strategies consistent with the needs of each individual.
Article
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Öğrenme analitikleri eğitim ortamlarının ve paydaşlarının objektif ölçümlerle anlaşılabilmesine imkân sağlaması, bu ortamlarda yaşanan sorunlara çözüm sunması ve bu ortamların daha verimli hale getirilmesine yönelik yeni yaklaşımlar sunması nedeniyle üzerine dikkatleri çeken bir konu haline gelmiştir. Öğrenme analitiği göstergeleri ise, henüz öğrenme problemleri ile ilişkilendirilmemiş, ancak e-öğrenme ortamlarında kullanılabilecek türde analitiklerdir. Bir bakıma öğrenme analitiği adaylarıdır. Bu çalışmanın amacı gelişim aşamasında görülen öğrenme analitiklerinin temellerine değinmenin yanı sıra, araştırmacı ve uygulamacılara e-öğrenme ortamlarında kullanabilecekleri öğrenme analitiği göstergelerine ilişkin bir liste sunmaktır. Çalışmada, alanyazın taramasından elde edilen göstergelerin yanı sıra Delphi tekniği kullanılarak uzmanların fikir birliğine vardıkları göstergelere de ulaşılmıştır. Çalışma için geliştirilen çevrimiçi platform aracılığıyla yürütülen Delphi panelleri yaklaşık 1 yıl sürmüş, 22 uzman ile başlayan süreç 11 uzman ile 4 tur sonunda tamamlanmıştır. Çalışmada demografik, betimsel ve algoritmik üst başlıklarında toplam 41 maddeden oluşan öğrenme analitiği göstergesine ulaşılmıştır. Bu listenin gerek öğrenme analitiklerini kullanarak öğrenme ortamlarında nesnel kararlar almaya çalışan uygulayıcılara gerekse de öğrenme analitiği ile ilgili konularda çalışmayı düşünen araştırmacılara katkı sağlayacağı düşünülmektedir.
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We are in the middle of rapid change in the fields of digitalization and automation. The Covid-19 pandemic accelerated the industry 4.0 work revolution by shifting people to a remote mode at work wherever it is possible. At the same time, the younger generations entering higher degree studies demand more personalized solutions in their learning paths. Haaga-Helia University of Applied Sciences has been developing a digitalized edtech tool Wihi to support students’ personalized thesis process and help supervisors to monitor multiple thesis projects. Wihi represents new kind of process-centric philosophy where a student’s learning process and a supervisor’s process are combined. While used two academic years so far, it was time to review what has been achieved, and especially, how students perceive the support of the system and the approach it represents. To find that out, we conducted a survey with structured and open-ended questions. The target group was the students who were in the thesis writing process or had recently completed it. The results reveal that Wihi supports students’ thesis project and enables personalized learning approach. However, Wihi’s features are used in different efficacy and there are also some challenges to be taken into account in further development and research.
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Purpose The presented research explored artificial intelligence (AI) application in the learning and development (L&D) function. Although a few studies reported AI and the people management processes, a systematic and structured study that evaluates the integration of AI with L&D focusing on scope, adoption and affecting factors is mainly absent. This study aims to explore L&D-related AI innovations, AI’s role in L&D processes, advantages of AI adoption and factors leading to effective AI-based learning following the analyse, design, develop, implement and evaluate approach. Design/methodology/approach The presented research has adopted a systematic literature review method to critically analyse, synthesise and map the extant research by identifying the broad themes involved. The review approach includes determining a time horizon, database selection, article selection and article classification. Databases from Emerald, Sage, Francis and Taylor, etc. were used, and the 81 research articles published between 1996 and 2022 were identified for analysis. Findings The result shows that AI innovations such as natural language processing, artificial neural networks, interactive voice response and text to speech, speech to text, technology-enhanced learning and robots can improve L&D process efficiency. One can achieve this by facilitating the articulation of learning module, identifying learners through face recognition and speech recognition systems, completing course work, etc. Further, the result also shows that AI can be adopted in evaluating learning aptitude, testing learners’ memory, tracking learning progress, measuring learning effectiveness, helping learners identify mistakes and suggesting corrections. Finally, L&D professionals can use AI to facilitate a quicker, more accurate and cheaper learning process, suitable for a large learning audience at a time, flexible, efficient, convenient and less expensive for learners. Originality/value In the absence of any systematic research on AI in L&D function, the result of this study may provide useful insights to researchers and practitioners.
Chapter
The dispersion of new technologies into education opens up new perspectives for the assessment of students’ performance, not only in their knowledge but also in their skills. One of these perspectives is the use of systems that enable adaptive assessment, i.e., personalized (mostly online) assessment according to the actual position of each student in the knowledge or skills hierarchy. The general presumption of this approach is that each student solves tasks according to his/her previous success – if the student fails, then s/he is presented with lower-level tasks; if s/he succeeds, s/he proceeds to more complicated tasks. Students’ progress is supported by the hints available and in particular by the structure of tasks, as easier tasks support the solution of the more complicated ones according to the theory of pedagogical scaffolding. The chapter first discusses a general framework for the use of adaptive assessment and then presents a practical example of the use of the framework to assess the level of map skills – the online adaptive application Mapwork.education. Following a general description of the application, various possibilities of its use in geography lessons are outlined.
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Learning Analytics techniques are widely used to improve students’ performance. Data collected from students’ assessments are helpful to predict their success and questionnaires are extensively adopted to assess students’ knowledge. Several mathematical models studying the correlation between students’ hidden skills and their performance to questionnaires’ items have been introduced. Among them, Non-negative matrix factorizations (NMFs) have been proven to be effective in automatically extracting hidden skills, a time-consuming activity that is usually tackled manually prone to subjective interpretations. In this paper, we present an intelligent data analysis approach based upon NMF. Data are collected from a competition, namely MathsChallenge, performed by the University of Foggia. In 2021 the competition has been held, for the first time, online due to the Covid-19 pandemic.
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This work focuses on studying the relationship that existed between the use of the learning management system (LMS) and the academic performance of the students of the Jorge Basadre Grohmann National University of Tacna-Perú. For this, we use the data provided by the LMS (access virtual classroom) and the university's academic management system (grades). For that, we perform various classification machine learning algorithms to predict academic performance with two classes SATISFACTORY or POOR where Gradient Boosted Trees algorithm had the best accuracy 91.79%. However, with three classes, SATISFACTORY, REGULAR AND POOR, Random Forest algorithm had the best accuracy of 89.26%.
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The Handbook of Learning Analytics is designed to meet the needs of a new and growing field. It aims to balance rigor, quality, open access and breadth of appeal and was devised to be an introduction to the current state of research. The Handbook is a snapshot of the field in 2017 and features a range of prominent authors from the learning analytics and educational data mining research communities. The chapters have been peer reviewed by committed members of these fields and are being published with the endorsement of both the Society for Learning Analytics Research and the International Society for Educational Data Mining. We hope you will find the Handbook of Learning Analytics a useful and informative resource.
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Research report of the ProLearn Network of Excellence (IST 507310), Deliverable 1.11
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Learning Analytics can provide powerful tools for teachers in order to support them in the iterative process of improving the effectiveness of their courses and to collaterally enhance their students' performance. In this paper, we present the theoretical background, design, implementation, and evaluation details of eLAT, a Learning Analytics Toolkit, which enables teachers to explore and correlate learning object usage, user properties, user behavior, as well as assessment results based on graphical indicators. The primary aim of the development of eLAT is to process large data sets in microseconds with regard to individual data analysis interests of teachers and data privacy issues, in order to help them to self-reflect on their technology-enhanced teaching and learning scenarios and to identify opportunities for interventions and improvements.
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Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.
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Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.
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This paper presents CourseVis, a system that takes a novel approach of using Web log data generated by course management systems (CMSs) to help instructors become aware of what is happening in distance learning classes. Specifically, techniques from information visualization (IV) are employed to graphically render complex, multidimensional student tracking data. Several graphical representations are generated to help distance learning instructors get a better understanding of social, behavioural, and cognitive aspects related to learners. The evaluation of CourseVis shows that it can help instructors to quickly identify tendencies in their classes and discover individuals that might need special attention. This suggests that the effectiveness of CMSs can be improved by integrating IV techniques to generate appropriate graphical representations, similar to those produced in CourseVis.
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Knowledge Tracing is perhaps the most widely used student model m the field of educational data mining. In this paper we report on the effects of using only a subset of data m trauung the Bayesian Network that represents this student model. The standard practice is to use all of the students data for a given skill to fit the model. We analyze two datasets: one from the Algebra Cognitive tutor and the other from the Genetics Cognitive tutor. We found that m both datasets. the difference in accuracy between using all the students' data versus only the most recent 15 data points of each student was not significantly different. Using only 15 responses however, resulted in an EM trauung time which was 15 times faster than usmg all data. This result suggests that the Knowledge Tracing model needs only a small range of data in order to learn reliable parameters. The implications of tins result is a substantial savings in model training time that allows for more complex models to be fit or individualized models to be tramed online.
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Intelligent tutoring systems that utilize Bayesian Knowledge Tracing (KT) have the ability to predict student performance well. However, models currently in use do not consider that a student performing on ITS may not be finishing their work in the same day. We looked at KTs predictions on student responses where a day or more had elapsed smce the previous response and found that KT consistently over predicted these data points in particular. We made two hypotheses to explain the over prediction behavior: 1) the student forgot since the last time on the tutor and 2) the student made a mistake (or slipped) on that first question of the day. We developed two models: KT-Forget and KT-$hp. modifications on Knowledge Tracing, to represent these two hypotheses. We evaluated and compared the performance of the KT-slip. KT-Forget and regular KT model by calculating prediction residuals and Area Under Curve (AUC) on a Cognitive Tutor and ASSISTments dataset. The results showed that a significant improvement was obtained on the overall prediction by our KT-Forget model, suggesting that forgetting is the more likely cognitive explanation for the data and that there is a place for modeling forgetting, something that has not common practice in student modeling.
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This paper demonstrates the generality of the hidden Markov model approach for exploratory sequence analysis by applying the methodology to study students ’ learning behaviors in a new domain, i.e., an asynchronous, online environment that promotes an explicit inquiry cycle while permitting a great deal of learner control. Our analysis demonstrates that the high-performing students have more linear learning behaviors, and that their behaviors remain consistent across different study modules. We also compare our approach to a process mining approach, and suggest how they may complement one another. 1
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We study how student behaviors associated with disengagement differ between different school settings. Towards this, we investigate the variation in the frequency of off-task behavior, gaming the system, and carelessness in an urban school, a rural school, and a suburban school in the United States of America. This analysis is conducted by applying automated detectors of these behaviors to data from students using the same Cognitive Tutor educational software for high school Geometry, across an entire school year. We find that students in the urban school go off-task and are careless significantly more than students in the rural and suburban schools. Differences between schools in terms of gaming the system are less stable. These findings suggest that some of the differences in achievement by school type may stem from differences in engagement and problem behaviors.
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Predicting student performance (PSP) is one of the educational data mining task, where we would like to know how much knowledge the students have gained and whether they can perform the tasks (or exercises) correctly. Since the student’s knowledge improves and cumulates over time, the sequential (temporal) effect is an important information for PSP. Previous works have shown that PSP can be casted as rating prediction task in recommender systems, and therefore, factorization techniques can be applied for this task. To take into account the sequential effect, this work proposes a novel approach which uses tensor factorization for forecasting student performance. With this approach, we can personalize the prediction for each student given the task, thus, it can also be used for recommending the tasks to the students. Experimental results on two large data sets show that incorporating forecasting techniques into the factorization process is a promising approach.
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While students ’ skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [1, 4, 6]. These estimates can be clustered to generate groups of similar students. Often hierarchical agglomerative clustering or k-means clustering is utilized, requiring, for K skills, the specification of 2 K clusters. The number of skill set profiles/clusters can quickly become computationally intractable. Moreover, not all profiles may be present in the population. We present a flexible version of k-means that allows for empty clusters. We also specify a method to determine efficient starting centers based on the Q-matrix. Combining the two substantially improves the clustering results and allows for analysis of data sets previously thought impossible. 1
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Rare association rules are those that only appear infrequently even though they are highly associated with very specific data. In consequence, these rules can be very appropriate for using with educational datasets since they are usually imbalanced. In this paper, we explore the extraction of rare association rules when gathering student usage data from a Moodle system. This type of rule is more difficult to find when applying traditional data mining algorithms. Thus we show some relevant results obtained when comparing several frequent and rare association rule mining algorithms. We also offer some illustrative examples of the rules discovered in order to demonstrate both their performance and their usefulness in educational environments.
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As novel forms of educational software continue to be created, it is often difficult to understand a priori which ensemble of interaction behaviours is conducive to learning. In this paper, we describe a user modeling framework that relies on interaction logs to identify different types of learners, as well as their characteristic interaction behaviours and how these behaviours relate to learning. This information is then used to classify new learners, with the long term goal of providing adaptive interaction support when behaviours detrimental to learning are detected. In previous research, we described a proof-of-concept version of this user modeling approach, based on unsupervised clustering and class association rules. In this paper, we describe and evaluate an improved version, implemented in a comprehensive user-modeling framework that streamlines the application of the various phases of the modeling process.
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Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from one day of interaction and tested with data from a later day. In terms of predicting what state a student was in during any 2 second period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. Further, the prediction accuracy using combined cognitive model and fMRI signal showed superadditivity of accuracies when using either cognitive model or fMRI signal alone.
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Dynamic assessment (DA) has been advocated as an interactive approach to conducting assessments to students in the learning systems. Sternberg and others proposed to give students tests to see how much assistance it takes a student to learn a topic; and to use as a measure of their learning gain. To researchers in the ITS community, it comes as no surprise that measuring how much assistance a student needs to complete a task successfully is probably a good indicator of this lack of knowledge. However, a cautionary note is that conducting DA takes more time than simply administering regular test items to students. In this paper, we report a study analyzing 40-minutes data of totally 1,392 students from two school years. The result suggests that for the purpose of assessing student performance, it is more efficient for students to take DA than just having practice items.
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The focus of this paper is to delineate and discuss design considerations for supporting teachers' dynamic diagnostic decision-making in classrooms of the 21st century. Based on the Next Generation Teaching Education and Learning for Life (NEXT-TELL) European Commission integrated project, we envision classrooms of the 21st century to (a) incorporate 1:1 computing, (b) provide computational as well as methodological support for teachers to design, deploy and assess learning activities and (c) immerse students in rich, personalized and varied learning activities in information ecologies resulting in high-performance, high-density, high-bandwidth, and data-rich classrooms. In contrast to existing research in educational data mining and learning analytics, our vision is to employ visual analytics techniques and tools to support teachers dynamic diagnostic pedagogical decision-making in real-time and in actual classrooms. The primary benefits of our vision is that learning analytics becomes an integral part of the teaching profession so that teachers can provide timely, meaningful, and actionable formative assessments to on-going learning activities in-situ. Integrating emerging developments in visual analytics and the established methodological approach of design-based research (DBR) in the learning sciences, we introduce a new method called "Teaching Analytics" and explore a triadic model of teaching analytics (TMTA). TMTA adapts and extends the Pair Analytics method in visual analytics which in turn was inspired by the pair programming model of the extreme programming paradigm. Our preliminary vision of TMTA consists of a collocated collaborative triad of a Teaching Expert (TE), a Visual Analytics Expert (VAE), and a Design-Based Research Expert (DBRE) analyzing, interpreting and acting upon real-time data being generated by students' learning activities by using a range of visual analytics tools. We propose an implementation of TMTA using open learner models (OLM) and conclude with an outline of future work
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In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or Each-Movie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for learning. We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to improve the performance of recommendation algorithms.
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We present new ways of detecting semantic relations between learning resources, e. g. for recommendations, by only taking their usage but not their content into account. We take concepts used in linguistic lexicology and transfer them from their original field of application, i. e. sequences of words, to the analysis of sequences of resources extracted from user activities. In this paper we describe three initial experiments, their evaluation and further work.
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Video lecture capture is rapidly being deploying in higher-education institutions as a means of increasing student learning, outreach, and experience. Understanding how learners use these systems and relating this use back to pedagogical and institutional goals is a hard issue that has largely been unexplored. This work describes a novel web-based lecture presentation system which contains fine-grained user tracking features. These features, along with student surveys, have been used to help analyse the behaviour of hundreds of students over an academic term, quantifying both the learning approaches of students and their perceptions on learning with lecture capture.
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Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential patterns of interaction. Transitive closure of these associograms creates sociograms, to which existing network analytic techniques may be applied, yielding aggregate results that can then be interpreted by reference to the other levels of analysis. We discuss how the analytic hierarchy bridges between levels of analysis and theory.
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There is great interest in assessing student learning in unscripted, open-ended environments, but students' work can evolve in ways that are too subtle or too complex to be detected by the human eye. In this paper, I describe an automated technique to assess, analyze and visualize students learning computer programming. I logged hundreds of snapshots of students' code during a programming assignment, and I employ different quantitative techniques to extract students' behaviors and categorize them in terms of programming experience. First I review the literature on educational data mining, learning analytics, computer vision applied to assessment, and emotion detection, discuss the relevance of the work, and describe one case study with a group undergraduate engineering students
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The action analytics of the future will better assess students’ competencies. Using individualized planning, advising, and best practices from cradle to career, these action analytics solutions will align interventions to facilitate retention and transitions and will fully maximize learners’ success.
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This article is a companion piece to the article “Action Analytics: Measuring and Improving Performance That Matters in Higher Education,” which describes the emergence of a new generation of tools, solutions, and behaviors that are giving rise to more powerful and effective utilities through which colleges and universities can measure performance and provoke pervasive actions to improve it.1 We call this new class of tools, solutions, and behaviors action analytics.2 The “Action Analytics” article provides examples of emerging analytics applications, as well as explanations of how open architecture technologies are lending themselves to a “cloud” of fresh applications and solutions that are not held hostage to the existing “stack” of ERP and other enterprise applications. The article also describes the imperative to change organizational capacity, culture, and behavior and to invent new measures and key performance indicators (KPIs) that are more appropriate to the new skills and habits of mind existing in our “flattening,” fast-changing world.
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Recently, interest in how this data can be used to improve teaching and learning has also seen unprecedented growth and the emergence of the field of learning analytics. In other fields, analytics tools already enable the statistical evaluation of rich data sources and the identification of patterns within the data. These patterns are then used to better predict future events and make informed decisions aimed at improving outcomes (Educause, 2010). This paper reviews the literature related to this emerging field and seeks to define learning analytics, its processes, and its potential to advance teaching and learning in online education.
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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
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Teachers Investigate Their Work introduces the methods and concepts of action research through examples drawn from studies carried out by teachers. The book is arranged as a handbook with numerous sub-headings for easy reference and fourty-one practical methods and strategies to put into action, some of them flagged as suitable `starters'. Throughout the book, the authors draw on their international practical experience of action research, working in close collaboration with teachers. It is an essential guide for teachers, senior staff and co-ordinators of teacher professional development who are interested in investigating their own practice in order to improve it. © 1993 Herbert Altrichter, Peter Posch and Bridget Somekh. All rights reserved.
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This chapter explores the potential for improving long-term learning by exploiting the large amounts of data that we can readily collect about learners. We present examples of interfaces to long-term learner models that illustrate both the challenges and potential for them. The challenges include the creation of a suitable technical framework as well as associated interfaces to support a learner in transforming arbitrary collections of learning data into a lifelong learner model. We explain how this can provide new ways to support both the learner and applications in personalizing learning over the long term, taking account of the individual's long-term development. Lifelong learning deals with the full breadth of learning, be it in a formal classroom or outside (Longworth, 2003), over long periods of time. This contrasts with the current norm for educational technologies, where the learner interacts with many computer-based tools, each operating independently of the others. Each collects its own data about the learner; this means that there are many independent silos of information about the learner and their learning progress. Commonly, one learning tool holds the data associated with learning a very specific skill, or a single subject, perhaps running over weeks or months. Such silos reduce the possibility for making use of this aggregated data over the long term.
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Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.
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In responding to internal and external pressures for accountability in higher education, especially in the areas of improved learning outcomes and student success, IT leaders may soon become critical partners with academic and student affairs. IT can help answer this call for accountability through "academic analytics," which is emerging as a new tool for a new era. Analytics marries large data sets, statistical techniques, and predictive modeling. It could be thought of as the practice of mining institutional data to produce actionable "intelligence." Today, analytics is most often used in higher education for administrative decisions--from delivering the targeted number and quality of a freshman class to cultivating likely donors. However, the use of analytics will likely grow in high-stakes areas such as academic success. Whether the catalyst for adoption is a call for accountability from outside of higher education or the need for scorecards or decision-making models from within, analytics is in higher education's future. To prepare, IT and institutional leaders need to begin to understand analytics--as well as the changes that may be required in data standards, tools, processes, organizations, policies, and institutional culture. For institutions to be successful in academic analytics projects, IT leaders must build a coalition of people. (Contains 21 notes.)
Article
Academic analytics helps address the public's desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide. Purdue University's Signals project applies the principles of analytics widely used in business intelligence circles to the problem of improving student success within a course and, hence, improving the institution's retention and graduation rates over time. Through its early stages, the Signals project's success has demonstrated the potential of academic analytics. Those early efforts have led to additional projects to develop: (1) Student success algorithms (SSAs) customized by course; (2) Intervention messages sent to students; and (3) New strategies for identifying students at risk. The premise behind Signals is fairly simple--utilize the data collected by instructional tools to determine in real time which students might be at risk, partially indicated by their effort within a course. Through analytics, the institution mines large data sets continually collected by these tools and applies statistical techniques to predict which students might be falling behind. The goal is to produce "actionable intelligence"--in this case, guiding students to appropriate help resources and explaining how to use them. Early reviews by administrators, faculty, and students have been positive, as has empirical data on the system's impact. The Signals system is based on a Purdue-developed SSA designed to provide students early warning--as early as the second week of the semester--of potential problems in a course by providing near real-time status updates of performance and effort in a course. Each update provides the student with detailed, positive steps to take in averting trouble. By no means is Purdue unique in its interest in academic analytics. Institutions across the world, large and small, public and private, research and teaching, have begun forays into various data source modeling strategies in an effort to find actionable data to support their goals. This article offers a snapshot of the experience at Purdue. (Contains 4 figures and 5 endnotes.)
Book
Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making. Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.
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Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.
Conference Paper
Student modeling is broadly used in educational data mining and intelligent tutoring systems for making scientific discoveries and for guiding instruction. For both of these goals, having high model accuracy is important, and researchers have incorporated a variety of features into student models. However, since different techniques use various features, when evaluating those approaches, we could not easily figure out what is key for a high predictive accuracy: the model or the features. In this paper, to establish such knowledge, we performed empirical studies varying which features the models considered such as items, skills, and transfer models. We found that item difficulty is a better predictor than skill difficulty or student proficiencies on the transfer model. Moreover, we evaluated two versions of the PFA model; the one with item difficulty resulted in slightly higher predictive accuracy than the one with skill difficulty. In addition, prior work has shown that considering student overall proficiencies, not just those thought to be important by the transfer model, works substantially better on ASSISTments data. However, in this study, we failed to find consistency of this phenomenon on the data collected from the Cognitive Tutor.
Conference Paper
Current peer-review software lacks intelligence for responding to students ’ reviewing performance. As an example of an additional intelligent assessment component to such software, we propose an evaluation system that generates assessment on reviewers ’ reviewing skills regarding the issue of problem localization. We take a data mining approach, using standard supervised machine learning to build classifiers based on attributes extracted from peer-review data via Natural Language Processing techniques. Our work successfully shows it is feasible to provide intelligent support for peer-review systems to assess students ’ reviewing performance fully automatically. 1
Conference Paper
We present an analysis of activity on iSpot, a website supporting participatory learning about wildlife through social networking. A sophisticated and novel reputation system provides feedback on the scientific expertise of users, allowing users to track their own learning and that of others, in an informal learning context. We find steeply unequal long-tail distributions of activity, characteristic of social networks, and evidence of the reputation system functioning to amplify the contribution of accredited experts. We argue that there is considerable potential to apply such a reputation system in other participatory learning contexts.
Conference Paper
The Social Networks Adapting Pedagogical Practice (SNAPP) tool was developed to provide instructors with the capacity to visualise the evolution of participant relationships within discussions forums. Providing forum facilitators with access to these forms of data visualisations and social network metrics in 'real-time', allows emergent interaction patterns to be analysed and interventions to be undertaken as required. SNAPP essentially serves as an interaction diagnostic tool that assists in bringing the affordances of 'real-time' social network analysis to fruition. This paper details the functional features included in SNAPP 2.0 and how they relate to learning activity intent and participant monitoring. SNAPP 2.0 includes the ability to view the evolution of participant interaction over time and annotate key events that occur along this timeline. This feature is useful in terms of monitoring network evolution and evaluating the impact of intervention strategies on student engagement and connectivity. SNAPP currently supports discussion forums found in popular commercial and open source Learning Management Systems (LMS) such as Blackboard, Desire2Learn and Moodle and works in both Internet Explorer and Firefox.