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Adopting Trust in Learning Analytics Infrastructure: A Structured Literature Review
Abstract
One key factor for the successful outcome of a Learning Analytics (LA) infrastructure is the ability to decide which software architecture concept is necessary. Big Data can be used to face the challenges LA holds. Additional challenges on privacy rights are introduced to the Europeans by the General Data Protection Regulation (GDPR). Beyond that, the challenge of how to gain the trust of the users remains. We found diverse architectural concepts in the domain of LA. Selecting an appropriate solution is not straightforward. Therefore, we conducted a structured literature review to assess the state-of-the-art and provide an overview of Big Data architectures used in LA. Based on the examination of the results, we identify common architectural components and technologies and present them in the form of a mind map. Linking the findings, we are proposing an initial approach towards a Trusted and Interoperable Learning Analytics Infrastructure (TIILA).
... To the best of our knowledge, nine new data infrastructures have been proposed in MMLA after our review [Schneider et al., 2018, Munoz et al., 2018, Tamura et al., 2019, Ciordas-Hertel et al., 2019, Huertas Celdrán et al., 2020, Camacho et al., 2020, Domínguez et al., 2021, Serrano Iglesias et al., 2021, Slupczynski and Klamma, 2021. The main focus of eight of these infrastructures is to solve other aspects rather than following a standard data processing pipeline (for example, educational aspects -individual learner's engagement [Camacho et al., 2020], smart learning environments [Serrano Iglesias et al., 2021], inclusive learning [Tamura et al., 2019], oral presentations [Munoz et al., 2018], and customizable learning experience [Schneider et al., 2018]; technical aspects -dynamic deployment of MMLA instances in smart classroom [Huertas Celdrán et al., 2020], using AI and blockchain [Slupczynski and Klamma, 2021], and wearables [Camacho et al., 2020]; or adoption aspects -at institutional level [Domínguez et al., 2021]). ...
... The main focus of eight of these infrastructures is to solve other aspects rather than following a standard data processing pipeline (for example, educational aspects -individual learner's engagement [Camacho et al., 2020], smart learning environments [Serrano Iglesias et al., 2021], inclusive learning [Tamura et al., 2019], oral presentations [Munoz et al., 2018], and customizable learning experience [Schneider et al., 2018]; technical aspects -dynamic deployment of MMLA instances in smart classroom [Huertas Celdrán et al., 2020], using AI and blockchain [Slupczynski and Klamma, 2021], and wearables [Camacho et al., 2020]; or adoption aspects -at institutional level [Domínguez et al., 2021]). Only one infrastructure is focused on following a standard data processing pipeline, but that is from the lens of the 3Vs of Big Data (Volume, Velocity, and Veracity) to optimize the scalability and performance issues of a technical solution [Ciordas-Hertel et al., 2019]. Hence, the limitation highlighted in our previous review remains the same even though nine new infrastructures have been proposed: none of the data infrastructures in MMLA follow a standard data processing pipeline while processing multimodal educational data. ...
... We reviewed 18 data infrastructures in MMLA (nine from our last review [Di Mitri et al., 2017, Muñoz-Cristóbal et al., 2018, Fiaidhi, 2014, Domínguez and Chiluiza, 2016, Ruffaldi et al., 2016, Harrer, 2013, Berg et al., 2016, Wagner et al., 2011, Segal et al., 2017 and nine proposed after our review [Schneider et al., 2018, Munoz et al., 2018, Tamura et al., 2019, Ciordas-Hertel et al., 2019, Huertas Celdrán et al., 2020, Camacho et al., 2020, Domínguez et al., 2021, Serrano Iglesias et al., 2021, Slupczynski and Klamma, 2021). We started our review by finding out how many learning tools were supported in the proposals. ...
Multimodal Learning Analytics (MMLA) solutions aim to provide a more holistic picture of a learning situation by processing multimodal educational data. Considering contextual information of a learning situation is known to help in providing more relevant outputs to educational stakeholders. However, most of the MMLA solutions are still in prototyping phase and dealing with different dimensions of an authentic MMLA situation that involve multiple cross-disciplinary stakeholders like teachers, researchers, and developers. One of the reasons behind still being in prototyping phase of the development lifecycle is related to the challenges that software developers face at different levels in developing context-aware MMLA solutions. In this paper, we identify the requirements and propose a data infrastructure called CIMLA. It includes different data processing components following a standard data processing pipeline and considers contextual information following a data structure. It has been evaluated in three authentic MMLA scenarios involving different cross-disciplinary stakeholders following the Software Architecture Analysis Method. Its fitness was analyzed in each of the three scenarios and developers were interviewed to assess whether it meets functional and non-functional requirements. Results showed that CIMLA supports modularity in developing context-aware MMLA solutions and each of its modules can be reused with required modifications in the development of other solutions. In the future, the current involvement of a developer in customizing the configuration file to consider contextual information can be investigated.
... Sie überführt die formal-mathematische Stringenz der Psychometrie in direkt verwertbare Aussagen darüber, was getestete Personen wissen und können und damit wie kompetent sie sind.Außer Frage steht, dass Learning Analytics und Kompetenzdiagnostik in Deutschland Hand in Hand mit den europäischen (DSGVO) und deutschen Datenschutzrichtlinien und ethischen Werten umgesetzt werden müssen. Neben technischen Lösungen zu einer datenschutzkonformen Verarbeitung von Prozessdaten aus Lernumgebungen(Ciordas-Hertel et al. 2019;Ciordas-Hertel et al. 2020), habenHansen, Rensing, Hermann und Drachsler (2020) einen Verhaltenskodex für Trusted Learning Analytics (TLA) veröffentlicht. Auf der Seite des Datenschutzes benennt der Kodex die aktuelle Rechtsprechung der DSGVO und erläutert diese für die Nutzung von Daten in der Bildungspraxis anhand sieben Prinzipien: 1. Zustimmung zur Datenerhebung, 2. Maxime der Datensparsamkeit, 3. Regelung der Zusammenarbeit mit Dritten, 4. Regelungen zur Datenlöschung, 5. Ermöglichung des Zugangs zu den Daten, 6. Transparenz der Datenquellen und 7. die Verwendung von Daten für Forschungszwecke. ...
Im Frühjahr 2020 wurden Schulen unerwartet vor die Herausforderung gestellt, Unterricht und Schulentwicklung vor dem Hintergrund kontinuierlicher pandemiebedingter Disruptionen zu ermöglichen. Unterricht vor Ort wurde ersetzt durch digitale Formate des Lernens und der Kommunikation auf Distanz.
Für die Bildungspraxis erweisen sich dabei die Herausforderungen im Bereich der digitalen Schulverwaltung, des digitalen Lernens und der Diagnostik von Lernfortschritten als besonders relevant. Insbesondere die computergestützte Diagnostik bietet großes Potenzial, um Erkenntnisse nicht nur über Lernergebnisse, sondern auch Lernprozesse zu generieren. Im Bereich der Bildungsforschung interessiert, wie Lernen durch digitale Medien gestaltet werden kann und wie die dabei generierten Daten für die Bildungspraxis gewinnbringend genutzt werden können.
Dieser Beitrag beschreibt die Herausforderungen und Potenziale, die sich im Bereich von computerbasierter, lernbegleitender Diagnostik gegenwärtig zeigen. Diese liegen insbesondere in der flächendeckenden Einführung entsprechender Instrumente in den Schulen sowie der Aus- und Weiterbildung von Lehrpersonen im Umgang mit diesen. Darauf aufbauend werden Bedarfe der Bildungspraxis und Desiderata der Bildungsforschung gegenübergestellt und auf Synergiepotenziale hingewiesen.
... Decision-making affected by decentralisation, lack of policies, trust in institutional commitment, and leadership have also been proposed as predictors of faculty and advisor trust in HEI in LA implementation (Klein et al., 2019a). A study by (Ciordas-Hertel et al., 2019) concluded that privacy is an important factor that affects trust in the LA infrastructure. ...
Learning analytics (LA) has gained increasing attention for its potential to improve different educational aspects (e.g., students’ performance and teaching practice). The existing literature identified some factors that are associated with the adoption of LA in higher education, such as stakeholder engagement and transparency in data use. The broad literature on information systems also emphasizes the importance of trust as a critical predictor of technology adoption. However, the extent to which trust plays a role in the adoption of LA in higher education has not been examined in detail in previous research. To fill this literature gap, we conducted a mixed method (survey and interviews) study aimed to explore how much teaching staff trust LA stakeholders (e.g., higher education institutions or third-parties) and LA technology, as well as the trust factors that could hinder or enable adoption of LA. The findings show that the teaching staff had a high level of trust in the competence of higher education institutions and the usefulness of LA; however, the teaching staff had a low level of trust in third parties that are involved in LA (e.g., external technology vendors) in terms of handling privacy and ethics-related issues. They also had a low level of trust in data accuracy due to issues such as outdated data and lack of data governance. The findings have strategic implications for institutional leaders and third parties in the adoption of LA by providing recommendations to increase trust, such as, improving data accuracy, developing policies for data sharing and ownership, enhancing the consent-seeking process, and establishing data governance guidelines. Therefore, this study contributes to the literature on the adoption of LA in HEIs by integrating trust factors.
... Furthermore, three main challenges have been identified, that is technical, educational and social challenges. Technical challenges are concerned with handling privacy concerns (Drachsler & Greller, 2016;Pardo & Siemens, 2014; or designing large-scale learning applications (Ciordas-Hertel et al., 2019;Sclater et al., 2015). Educational challenges are concerned with a higher degree of applicability (Baker, 2019), higher reliability (Kitto et al., 2018;Larrabee Sønderlund et al., 2019;Mahmoud et al., 2020;Scheffel et al., 2017), or better connections to learning science (Ahmad et al., 2022;Ferguson, 2012;Jivet et al., 2017). ...
Background
Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders' expectations of LA across Higher Education Institutions (HEIs) for large‐scale implementations that take their needs into account.
Objectives
This study aims to contribute to knowledge about individual LA expectations of European higher education students. It may facilitate the strategy of stakeholder buy‐in, the transfer of LA insights across HEIs, and the development of international best practices and guidelines.
Methods
To this end, the study employs a ‘Student Expectations of Learning Analytics Questionnaire’ (SELAQ) survey of 417 students at the Goethe University Frankfurt (Germany) Based on this data, Multiple Linear Regressions are applied to determine how these students position themselves compared to students from Madrid (Spain), Edinburgh (United Kingdom) and the Netherlands, where SELAQ had already been implemented at HEIs.
Results and Conclusions
The results show that students' expectations at Goethe University Frankfurt are rather homogeneous regarding ‘LA Ethics and Privacy’ and ‘LA Service Features’. Furthermore, we found that European students generally show a consistent pattern of expectations of LA with a high degree of similarity across the HEIs examined. European HEIs face challenges more similar than anticipated. The HEI experience with implementing LA can be more easily transferred to other HEIs, suggesting standardized LA rather than tailor‐made solutions designed from scratch.
... A study by Ciordas-Hertel et al. also searched the four databases IEEE, ScienceDirect, SpringerLink, and ACM, focusing on studies related to the keyword "Big Data and education" between 2015 and 2019. They examined 20 of these articles and found that the Hadoop cluster structure was the most used [Ciordas-Hertel et al. 2019]. In Wang and Zhao's study, modules were created on a Big Data cloud computing platform and user data was encrypted using the MD5 algorithm [Wang and Zhao, 2021]. ...
Information technologies have invaded every aspect of our lives. Distance education was also affected by this phase and became an accepted model of education. The evolution of education into a digital platform has also brought unexpected problems, such as the increase in internet usage, the need for new software and devices that can connect to the Internet. Perhaps the most important of these problems is the management of the large amounts of data generated when all training activities are conducted remotely. Over the past decade, studies have provided important information about the quality of training and the benefits of distance learning. However, Big Data in distance education has been studied only to a limited extent, and to date no clear single solution has been found. In this study, a Distributed File Systems (DFS) is proposed and implemented to manage big data in distance education. The implemented ecosystem mainly contains the elements Dynamic Link Library (DLL), Windows Service Routines and distributed data nodes. DLL codes are required to connect Learning Management System (LMS) with the developed system. 67.72% of the files in the distance education system have small file size (<=16 MB) and 53.10% of the files are smaller than 1 MB. Therefore, a dedicated Big Data management platform was needed to manage and archive small file sizes. The proposed system was designed with a dynamic block structure to address this shortcoming. A serverless architecture has been chosen and implemented to make the platform more robust. Moreover, the developed platform also has compression and encryption features. According to system statistics, each written file was read 8.47 times, and for video archive files, this value was 20.95. In this way, a framework was developed in the Write Once Read Many architecture. A comprehensive performance analysis study was conducted using the operating system, NoSQL, RDBMS and Hadoop. Thus, for file sizes 1 MB and 50 MB, the developed system achieves a response time of 0.95 ms and 22.35 ms, respectively, while Hadoop, a popular DFS, has 4.01 ms and 47.88 ms, respectively.
... Although the ethical debate for the data subjects vs. data controllers dilemma is of high and equal relevance, this study focuses only on the legal aspect of privacy concerns of LA. Preserving privacy of the data subjects while performing LA has been recognized in principle embraced by previous research [9,16,18,19,[31][32][33]. However, as [9] points out, the complexity of the field of education due to different legal obligations and rights of stakeholders could hinder the effective application of legal measures in real-life situations. ...
Personalized learning is one of the main focuses in 21st-century education, and Learning Analytics (LA) has been recognized as a supportive tool for enhancing personalization. Meanwhile, the General Data Protection Regulations (GDPR), which concern the protection of personal data, came into effect in 2018. However, contemporary research lacks the essential knowledge of how and in which ways the presence of GDPR influence LA research and practices. Hence, this study intends to examine the requirements for sustaining LA under the light of GDPR. According to the study outcomes, the legal obligations for LA could be simplified to data anonymization with consequences of limitations to personalized interventions, one of the powers of LA. Explicit consent from the data subjects (students) prior to any data processing is mandatory under GDPR. The consent agreements must include the purpose, types of data, and how, when and where the data is processed. Moreover, transparency of the complete process of storing, retrieving, and analysing data as well as how the results are used should be explicitly documented in LA applications. The need for academic institutions to have specific regulations for supporting LA is emphasized. Regulations for sharing data with third parties is left as a further extension of this study.
... For this reason, this paper presents Edutex, a software infrastructure that can leverage consumer smartwatches and smartphones for this purpose. Edutex is an implementation of the Trusted and Interoperable Infrastructure for Learning Analytics (TIILA) [12] with a specialization in mobile sensing through smart wearables. ...
Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.
... Additionally, various recurrent-(RNN) and convolutional neural network (CNN) architectures for discovering fake news on Twitter are proposed in (Ajao et al., 2018) The recent advent of massive distributed frameworks such as Apache Spark has enabled an in-depth analysis of Web trust (Ventocilla, 2019). Principles for the development of distributed trust analytics are given in (Ciordas-Hertel et al., 2019). Computational aspects of the Web trust when seen as a data intensive problem are examined in (Terzi et al., 2017). ...
Although trust is predominantly a human trait, it has been carried over to the Web almost since its very inception. Given the rapid Web evolution to a true melting pot of human activity, trust plays a central role since there is a massive number of parties interested in interacting in a multitude of ways but have little or even no reason to trust a priori each other. This has led to schemes for evaluating Web trust in contexts such as e-commerce, social media, recommender systems, and e-banking. Of particular interest in social networks are classification methods relying on network-dependent attributes pertaining to the past online behavior of an account. Since the deployment of such methods takes place at Internet scale, it makes perfect sense to rely on distributed processing platforms like Apache Spark. An added benefit of distributed platforms is paving the way algorithmically and computationally for higher order Web trust metrics. Here a Web trust classifier in MLlib, the machine learning library for Apache Spark, is presented. It relies on both the account activity but also on that of similar accounts. Three datasets obtained from topic sampling regarding trending Twitter topics serve as benchmarks. Based on the experimental results best practice recommendations are given.
... In a previously conducted structured literature review on LA infrastructure [7] , we found many papers with drafts of LA infrastructure. However, we found little information about the design considerations and whether and how the authors applied and maintained data protection and privacy in practice. ...
The digitalisation or virtualisation of lab equipment in higher education promises numerous benefits for all those involved. Economic benefits from sharing lab infrastructures, convenient remote access to labs anytime and anywhere, as well as the sharing and linking of lab-based lectures are just some of the advantages that come to mind when thinking of online lab infrastructures. However, the technical, didactical and organisational effort required to digitalise labs should not be underestimated. The different chapters of this book provide insights into these different aspects from the perspectives of both researchers and lecturers. With contributions by Hadi Adineh, Tobias Ableitner, Majsa Ammouriova, Jannicke Baalsrud Hauge, Massimo Bertolini, Martin Burghardt, Michael Canz, Juliana Castaneda, Jens Doveren, Matthias Ehlenz, Thomas Eppler, Giovanni Esposito, Peter Ferdinand, Matas Führer, Jens Glembin, Myriam Guedey, Felix Gers, Yasmin Hayat, Roland Heinrich, Karsten Henke, Clara Henkel, Birte Heinemann, Nils Höhner, Andrej Itrich, Marc Philipp Jensen, Valentin Kammerlohr, Rushed Kanawati, Abdelmajid Khelil, Michael Klein, Sebastian Koch, Johannes Kretzschmar, Jean-Vincent Loddo, Davide Mezzogori, Johannes Nau, Mattia Neroni, David Paradice, Angel A. Juan Perez, Anke Pfeiffer, Tobias Christian Piller, Paul Press, Steffen Prowe, Giovanni Romagnoli, Benedikt Reuter, Davide Reverberi, Peter Rödler, David Romero, David Schepkowski, Ulrik Schroeder, Jan Seedorf, Detlef Streitferdt, Peter Treffinger, Dieter Uckelmann and Gottfried Zimmermann.
Learning Analytics is a vast concept and a rapidly growing field in higher education used by professors to measure, collect and analyze digital learning records to improve learning, generate new pedagogies, and make decisions about technology-driven learning. The following article presents a mapping and systematic literature review on Learning Analytics and its link to the teaching skills carried out in university practice. The research process reviewed 7,886 articles during the period from 2016 to 2020. After applying the inclusion and exclusion criteria, 50 articles were analyzed in-depth under the dimensions of (1) purposes of Learning Analytics, (2) teaching competencies, and (3) teaching practice in higher education. This work provides a basis for identifying gaps and research opportunities related to the application of teaching competencies in the field of Learning Analytics and incorporating it into teaching practice in online tutoring.
This paper provides a literature review on security and privacy issues of big data. These issues are classified into three contexts; technological, organizational and environmental that is meant to facilitate future research. The main objectives of the review are to identify security and privacy issues of big data and to categorize the issues into a classification framework. The outcome of this review reveals that security and privacy issues of big data not only originate from technological deficiencies, but it may also be the outcome of organizational culture and environmental influences. At the end of review for each of the contexts, main issues were extracted and presented as potential factors that may affect organizational intention to adopt big data.
With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.
This article provides an overview description of the four-component instructional design system (4C/ID-model) developed originally
by van Merriënboer and others in the early 1990s (van Merriënboer, Jelsma, & Paas, 1992) for the design of training programs
for complex skills. It discusses the structure of training blueprints for complex learning and associated instructional methods.
The basic claim is that four interrelated components are essential in blueprints for complex learning: (a) learning tasks,
(b) supportive information, (c) just-in-time (JIT) information, and (d) part-task practice. Instructional methods for each
component are coupled to the basic learning processes involved in complex learning and a fully worked-out example of a training
blueprint for “searching for literature” is provided. Readers who benefit from a structured advance organizer should consider
reading the appendix at the end of this article before reading the entire article.
Data solutions in the teaching and learning space are in need of pro-active innovations in data management, to ensure that systems for learning analytics can scale up to match the size of datasets now available. Here, we illustrate the scale at which a Learning Management System (LMS) accumulates data, and discuss the barriers to using this data for in-depth analyses. We illustrate the exponential growth of our LMS data to represent a single example dataset, and highlight the broader need for taking a pro-active approach to dimensional modelling in learning analytics, anticipating that common learning analytics questions will be computationally expensive, and that the most useful data structures for learning analytics will not necessarily follow those of the source dataset.
The popularity of Massive Open Online Courses has been rapidly growing recently. However, the completion rates of MOOC appear to be quite low. Moreover, the learning quality is quite doubtful for administrators of Universities since there is no suitable tools to evaluate it. Benefitting from the online environment, MOOC platforms can collect and store a huge amount of data related to learning processes. We use Storm as the parallel computing tool to accomplish the data analysis of MOOC. Our research focuses on three types of learning quality evaluation: relationship between students’ forum participation and their academic performance, relationship between students’ forum emotion and their academic performance, relationship between students’ video seeking operation and their academic performance.
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