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What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics?
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Massive open online courses (MOOCs) are the road that led to a revolution and a new era of learning environments. Educational institutions have come under pressure to adopt new models that assure openness in their education distribution. Nonetheless, there is still altercation about the pedagogical approach and the absolute information delivery to the students. On the other side with the use of Learning Analytics, powerful tools become available which mainly aim to enhance learning and improve learners’ performance. In this chapter, the development phases of a Learning Analytics prototype and the experiment of integrating it into a MOOC platform, called iMooX will be presented. This chapter explores how MOOC stakeholders may benefit from Learning Analytics as well as it reports an exploratory analysis of some of the offered courses and demonstrates use cases as a typical evaluation of this prototype in order to discover hidden patterns, overture future proper decisions, and to optimize learning with applicable and convenient interventions.
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... platform. Source: [11] VI. SPECIAL FEATURES AND RESEARCH OF IMOOX.AT ...
... Therefore, it is simply consequent if iMooX.at has been used for some research purposes on this topic. Fig. 3 points out the whole process how Learning Analytics measurements were and are implemented at iMooX.at [11]. ...
iMooX.at is the Austrian MOOC platform founded in 2014. This platform offers free, openly licensed online courses for all, so called Massive Open Online Course (MOOCs). It aims to offer university education in an innovative and digital way.In this article, we will briefly look at the history of the platform and its main milestones till now. Finally, a few possible development steps will be pointed out and discussed.
... A model for prediction using learning analytics in MOOCs was done for finding the student retention in a course using an vector-based support vector machine (RTV-SVM). This model was used for notifying them well in advance about their progress in order to increase retention rate, improve the learning experience (Khalil & Ebner, 2016). Two courses were used to analyze the success rate depending on the weights assigned to each activity as per their adequate significance. ...
In the recent pandemic times, there was an impactful transformation in imparting education which required everyone to become online learners. There has been an exponential growth in the number of e-learners attending classes online and taking MOOCs courses. This has opened an avenue for research to analyze the emotion of e-learners through reviews of students to evaluate the learning outcomes and performance of the course. Most challenging task is to find the exact pulse of the e-learners’ emotions from the huge data of the e-learners reviews. The reviews on all online platforms are mostly textual and this qualitative data needs to be quantified for analysis. There is a necessity to propose contextual emotion detection of e-learners by extracting the relevant information which can be correlated to the performance of the course on e-learning platform. Further, it can be a recommendation system to the aspiring e-learners to make decision based on thesatisfaction index of previous e-learners. This paper leverages deep learning techniques to train various models for academic emotion detection using dataset E-Learners Academic Reviews (ELAR) prepared from online textual feedback of e-learners and MOOCs course reviews. The Bidirectional Encoder Representations from Transformers (BERT) transfer learning model used to detect the emotions outperformed the other models. This proposed method using ELAR dataset is a novel approach to identify the right emotion of e-learners from the course reviews available on e-learning platform. The results were discussed with a benchmark of ISEAR (International Survey on Emotion Antecedents and Reactions) and GoEmotion Dataset. Keywords : Academic emotions; Digital natives; Deep learning; E-learners; Textual emotions
... Unlike distance learning courses, which due to these and several other reasons were not readily accepted in the mainstream, MOOCs still have made some place in the mainstream education, even though it is still limited. 1 Open distance learning is a more advanced form of higher education because it resembles the openness exhibited by individuals in everyday life. ...
... A lot of development and research work is also being carried out regarding the monitoring of learners and the analysis of data in terms of learning analytics. The goal is to allow those responsible for MOOCs to identify potential for possible improvements (see Figure 8, Khalil & Ebner, 2016;Maier, Leitner & Ebner, 2019). ...
Based on the increasing demand for and promotion of Open Educational Resources (OER, see (UNESCO (2019), this chapter describes the objectives of Graz University of Technology (TU Graz) in Austria for good teaching. A description of how the impact of OER at TU Graz will be analysed and considerations around it is the central contribution. In addition, the effects, and potentials of selected OER initiatives of the university are described as examples and discussed as key potential for good teaching. For a better understanding of the role of OER at TU Graz, the national context of OER in the Austrian higher education landscape is described at the beginning of the chapter.
... Despite a broad acceptance of the various benefits of learning analytics within open, distance, and distributed educational systems to support improved retention rates and educational practices (see e.g., Khalil & Ebner, 2016b), there are few studies on learning analytics frameworks designed for use with ubiquitous mobile devices and SRL. Earlier research has shown that mobile technologies in education can be advantageous and that mobile apps can enhance students' abilities to self-regulate their learning (Broadbent et al., 2020). ...
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.
588 V10-N1 (ene-feb) 2025, pp 588-601 | Recibido: 31 de octubre del 2024-Aceptado: 03 de diciembre del 2024 (2 ronda rev.) Implementación efectiva de las tecnologías del aprendizaje y el conocimiento en el área de ciencias naturales en la educación secundaria Effective implementation of learning and knowledge technologies in the area of natural sciences in secondary education 1 ORCID: https://orcid.org/0009-0000-7579-8529 2 ORCID: https://orcid.org/0009-0005-9727-903X 3 ORCID: https://orcid.org/0009-0007-0178-5099 4 ORCID: https://orcid.org/0000-0001-7457-5323
Este estudio analiza la implementación efectiva de las Tecnologías del Aprendizaje y el Conocimiento (TAC) en el área de Ciencias Naturales en la Educación Secundaria. La investigación aborda el problema de que, a pesar de los beneficios documentados, la integración de las TAC en la enseñanza de las ciencias aún enfrenta desafíos como la falta de capacitación docente y la integración curricular inadecuada. El objetivo principal de la investigación es evaluar el impacto de la integración efectiva de las TAC en el mejoramiento del rendimiento académico en el área de Ciencias Naturales en el contexto ecuatoriano. Para ello, se realizó un diagnóstico inicial del nivel de conocimiento de los estudiantes, se implementaron herramientas digitales didácticas que integraban diferentes TAC, como simulaciones, animaciones y laboratorios virtuales, y finalmente se evaluó el progreso en el aprendizaje. Los resultados revelaron que la implementación efectiva de las TAC en la enseñanza de las Ciencias Naturales mejoró significativamente el rendimiento y la motivación de los estudiantes, así como su comprensión y retención de los conceptos clave. Estos hallazgos proporcionan evidencia empírica que respalda la adopción de las TAC en la educación científica, superando los desafíos existentes a través de una integración curricular adecuada y la capacitación docente. En conclusión, este estudio destaca el potencial de las TAC para transformar y enriquecer los procesos de enseñanza y aprendizaje en el área de Ciencias Naturales en la Educación Secundaria.
This paper discusses the general thesis that massive open online courses (in short MOOC), open educational resources (in short OER) and learning analytics are an impactful trio for future education, especially if combined. The contribution bases upon our practical experience as service providers and researchers in the department “Educational Technology” at Graz University of Technology (TU Graz) in Austria. The team members provide support to lecturers, teachers and researchers in these addressed fields for several years now, for example as host of the MOOC platform iMooX.at, providing only OER since 2015. Within this contribution, we will show, against some doubtful or conflicting opinions and positions, that (a) MOOCs are opening-up education; (b) learning analytics give insights and support learning, not only online learning, if implemented in MOOCs; and (c) that OER has the potential for sustainable resources, innovations and even more impact, especially if implemented in MOOCs.
This chapter reviews key terms in online language teaching and learning and familiarises the reader with the numerous, diverse variables involved in using a platform to teach languages online.
Online learning has proved its effectiveness in the last few years among a wide range of learners. Massive Open Online Courses (MOOCs) have revolutionized the shape of learning because they are considered to be a substitutional tool to the conventional educational system for many reasons, such as flexibility in timing and eliminating the economic and geographical constraints to the learners. MOOCs also enable learners from different cultures to communicate and share their knowledge through forums. Nevertheless, MOOCs are encountering several challenges that are required to be addressed, such as the higher dropout rates among learners at different phases of the course, and reduction in participation level of learners. In this chapter, we aim to address the most familiar four challenges and enhance the MOOCs experience through providing a framework of integrating a Learning Analytics technique and Intelligent Conversational Agent (LAICA) to improve the MOOCs experience for learners and educators.
Over the last ten years learning analytics (LA) has grown from a hypothetical future into a concrete field of inquiry and a global community of researchers and practitioners. Although the LA space may appear sprawling and complex, there are some clear through-lines that the new student or interested practitioner can use as entry points. Four of these are presented in this chapter, 1. LA as a concern or problem to be solved, 2. LA as an opportunity, 3. LA as field of inquiry and 4. the researchers and practitioners that make up the LA community. These four ways of understanding LA and its associated constructs, technologies, domains and history can hopefully provide a launch pad not only for the other chapters in this handbook but the world of LA in general. A world that, although large, is open to all who hold an interest in data and learning and the complexities that follow from the combination of the two.
Our project is motivated by the early drop out and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer granularity information to make more accurate predictions of dropout and performance. The results show that adding final-grained temporal or non-temporal information into behaviour features provides more predictive power in the early phases of a POSA MOOC. As a next step, we plan to determine if we could use these features to better profile students with unsupervised learning methods.
Many MOOCs initiatives continue to report high attrition rates among distance education students. This study investigates why students dropped out or failed their MOOCs. It also provides strategies that can be implemented to increase the retention rate as well as increasing overall student satisfaction. Through studying literature, accurate data analysis and personal observations, the most significant factors that cause high attrition rate of MOOCs are identified. The reasons found are lack of time, lack of learners’ motivation, feelings of isolation and the lack of interactivity in MOOCs, insufficient background and skills, and finally hidden costs. As a result, some strategies are identified to increase the online retention rate, and will allow more online students to graduate.
What happens when well-known universities offer online courses, assessments, and certificates of completion for free? Early descriptions of Massive Open Online Courses (MOOCs) have emphasized large enrollments, low certification rates, and highly educated registrants. We use data from two years and 68 open online courses offered by Harvard University (via HarvardX) and MIT (via MITx) to broaden the scope of answers to this question. We describe trends over this two-year span, depict participant intent using comprehensive survey instruments, and chart course participation pathways using network analysis. We find that overall participation in our MOOCs remains substantial and that the average growth has been steady. We explore how diverse audiences — including explorers, teachers-as-learners, and residential students — provide opportunities to advance the principles on which HarvardX and MITx were founded: access, research, and residential education.
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.
Teacher resistance, teacher accommodation, and teacher conformism informed instructional strategies that Mr. Jenkins used to prevent suspension. Mr. Jenkins’s instructional strategies were impacted by his resistance to dominant PBS ideology, accommodation of system constraints related to classroom disruptions and PBS, and conformism to the dominant ideology of teaching and learning culinary arts.
Massive open online courses (MOOCs) are now being used across the world to provide millions of learners with access to education. Many learners complete these courses successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern for platform providers and educators. In 2013, a team from Stanford University analysed engagement patterns on three MOOCs run on the Coursera platform. They found four distinct patterns of engagement that emerged from MOOCs based on videos and assessments. However, not all platforms take this approach to learning design. Courses on the FutureLearn platform are underpinned by a social-constructivist pedagogy, which includes discussion as an important element. In this paper, we analyse engagement patterns on four FutureLearn MOOCs and find that only two clusters identified previously apply in this case. Instead, we see seven distinct patterns of engagement: Samplers, Strong Starters, Returners, Mid-way Dropouts, Nearly There, Late Completers and Keen Completers. This suggests that patterns of engagement in these massive learning environments are influenced by decisions about pedagogy. We also make some observations about approaches to clustering in this context.
Massively Open Online Courses (MOOCs) have gained attention recently because of their great potential to reach learners. Substantial empirical study has focused on student persistence and their interactions with the course materials. However, most MOOCs include a rich textual dialogue forum, and these textual interactions are largely unexplored. Automatically understanding the nature of discussion forum posts holds great promise for providing adaptive support to individual students and to collaborative groups. This paper presents a study that applies unsupervised student understanding models originally developed for synchronous tutorial dialogue to MOOC forums. We use a clustering approach to group similar posts, compare the clusters with manual annotations by MOOC researchers, and further investigate clusters qualitatively. This paper constitutes a step toward applying unsupervised models to asynchronous communication, which can enable massive-scale automated discourse analysis and mining to better support students' learning.