Assessing Learning Progress and Evaluating Services

2021 ◽  
pp. 37-54
Author(s):  
Christine L. Weber ◽  
Cecelia Boswell ◽  
Wendy A. Behrens
Keyword(s):  
Learning Progress

scholarly journals Influence of MOOC learners discussion forum social interactions on online reviews of MOOC

2021 ◽  
Author(s):  
Bing Wu
Keyword(s):  
Social Interactions ◽  
Regression Models ◽  
Online Courses ◽  
Online Reviews ◽  
Discussion Forum ◽  
Massive Open Online Courses ◽  
Online Review ◽  
Discussion Forums ◽  
Processing Data ◽  
Learning Progress

AbstractAlthough some studies have explored massive open online courses (MOOCs) discussion forums and MOOC online reviews separately, studies of both aspects are insufficient. Based on the theory of self-determination, this paper proposes research hypotheses that MOOC learning progress has a direct impact on MOOC online reviews and an indirect influence on MOOC online reviews through social interactions in discussion forums, as well. Coursera the largest MOOC platform, is selected as the empirical research object, and data from learners who participated in the MOOC discussion forum and provided MOOC online reviews from August 2016 to December 2019 are obtained from the most popular course, “Machine Learning”. After processing, data from 4376 learners are obtained. Then, according to research hypotheses, multi regression models are constructed accordingly. The results show that the length of MOOC online review text is affected by the MOOC learning progress, the number of discussion forum posts, the number of follow, the online review sentiment and MOOC rating. This study highlights the main factors that affect MOOC online reviews. As a result, some suggestions are put forward for the construction of MOOC.

scholarly journals Ambiguity in European seasonal comparative research: how decisions on modelling shape results on inequality in learning?

2021 ◽  
Author(s):  
Laura A. Helbling ◽  
Martin J. Tomasik ◽  
Urs Moser
Keyword(s):  
Comparative Research ◽  
Social Performance ◽  
Real Data ◽  
Social Disparities ◽  
Conditional Learning ◽  
Summer Setback ◽  
Research Questions ◽  
Learning Progress ◽  
Modeling Strategy ◽  
Study Designs

AbstractSummer break study designs are used in educational research to disentangle school from non-school contributions to social performance gaps. The summer breaks provide a natural experimental setting that allows for the measurement of learning progress when school is not in session, which can help to capture the unfolding of social disparities in learning that are the result of non-school influences. Seasonal comparative research has a longer tradition in the U.S. than in Europe, where it is only at its beginning. As such, summer setback studies in Europe lack a common methodological framework, impairing the possibility to draw lines across studies because they differ in their inherent focus on social inequality in learning progress. This paper calls for greater consideration of the parameterization of “unconditional” or “conditional” learning progress in European seasonal comparative research. Different approaches to the modelling of learning progress answer different research questions. Based on real data and constructed examples, this paper outlines in an intuitive fashion the different dynamics in inequality that may be simultaneously present in the survey data and distinctly revealed depending on whether one or the other modeling strategy of learning progress is chosen. An awareness of the parameterization of learning progress is crucial for an accurate interpretation of the findings and their international comparison.

scholarly journals Monitoring online learners’ performance based on learning progress prediction

2021 ◽  
Author(s):  
Ria Arafiyah ◽  
Zainal A. Hasibuan ◽  
Harry Budi Santoso
Keyword(s):  
Online Learners ◽  
Learning Progress

scholarly journals Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering

2018 ◽  
Vol 19 (5) ◽  
Author(s):  
Antoine Van den Beemt ◽  
Joos Buijs ◽  
Wil Van der Aalst
Keyword(s):  
Data Science ◽  
Online Courses ◽  
Process Mining ◽  
Massive Open Online Courses ◽  
Structured Learning ◽  
Learning Behaviour ◽  
Specific Behaviour ◽  
Learning Progress ◽  
Tools And Techniques ◽  
Successful Students

The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students’ activities in a MOOC from the perspective of personal constructivism, which we operationalized as a combination of learning behaviour and learning progress. This study considers students’ data analyzed as per the MOOC Process Mining: Data Science in Action. We explore the relation between learning behaviour and learning progress in MOOCs, with the purpose to gain insight into how passing and failing students distribute their activities differently along the course weeks, rather than predict students' grades from their activities. Commonly-studied aggregated counts of activities, specific course item counts, and order of activities were examined with cluster analyses, means analyses, and process mining techniques. We found four meaningful clusters of students, each representing specific behaviour ranging from only starting to fully completing the course. Process mining techniques show that successful students exhibit a more steady learning behaviour. However, this behaviour is much more related to actually watching videos than to the timing of activities. The results offer guidance for teachers.

scholarly journals Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jose Maria Garcia-Garcia ◽  
Víctor M. R. Penichet ◽  
María Dolores Lozano ◽  
Juan Enrique Garrido ◽  
Effie Lai-Chong Law
Keyword(s):  
User Experience ◽  
Affective Computing ◽  
Educational Software ◽  
Control Group ◽  
Emotional States ◽  
Emotion Detection ◽  
Software Application ◽  
Software Applications ◽  
Learning Progress ◽  
The One

Affective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most important aspects regarding Affective Computing. In this paper, we present an educational software application that incorporates affective computing by detecting the users’ emotional states to adapt its behaviour to the emotions sensed. This way, we aim at increasing users’ engagement to keep them motivated for longer periods of time, thus improving their learning progress. To prove this, the application has been assessed with real users. The performance of a set of users using the proposed system has been compared with a control group that used the same system without implementing emotion detection. The outcomes of this evaluation have shown that our proposed system, incorporating affective computing, produced better results than the one used by the control group.

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