Analysis of Dynamic Resource Access Patterns in Online Courses

Hecking, Tobias; Ziebarth, Sabrina; Hoppe, H. Ulrich

doi:10.18608/jla.2014.13.4

Cited by 29 publications

(11 citation statements)

References 28 publications

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“…It is important to understand that all the learners may not have the same learning objective, which may give rise to different patterns of access. Investigating the relationship between learners and course materials can give better insight into the learners (Hecking, Ziebarth, & Hoppe, 2014). Relationships between learners based on identifying similarities in course material usage patterns can help reveal commonalities of each group.…”

Section: Identifying Learner Groups By Clusteringmentioning

confidence: 99%

Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

et al. 2016

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ABSTRACT:MOOCs attract a large number of learners with largely unknown diversity in terms of motivation, ability, and goals. To understand more about learners in highly technical engineering MOOCs, this study investigates patterns of learners' (n = 337) behaviour and performance in the Nanophotonic Modelling MOOC, offered through nanoHUB-U. The authors explored clusters of learner clickstream patterns using the k-means++ algorithm and found five clusters of learner behaviour, labelled according to learners' use of materials: Fully Engaged, Consistent Viewers, One-Week Engaged, Two-Week Engaged, and Sporadic learners. The Kruskal-Wallis nonparametric statistical test yielded a significant difference (p< 0.01) between learners' access of course materials in each cluster. The researchers then examined the participation and mean scores on course quizzes and exams for each learner group. One-Week Engaged learners, on average, scored significantly lower on the first week's assessment. Two-Week Engaged learners, on average, scored significantly lower on the second week's assessments. Other differences found in learners' participation and performance on quizzes and tests based on the five clusters are discussed. These findings suggest that some of the high dropout numbers in advanced MOOCs may be related to learners' performance on course assessments. In addition, integration of learner access to course material with course assessment scores provides a much richer understanding of learners in a MOOC.

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Section: Identifying Learner Groups By Clusteringmentioning

confidence: 99%

Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

et al. 2016

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“…Previous research on exam revision has explored the use of specific technologies or learning activities in revision, ranging from the use of electronic surveys [7], mobile tools [8], practice tests [9], understanding marking criteria [10], the reuse of materials encountered earlier in a module, podcast revision lecturer [11], and examination strategies [12]. More directly related to learning analytics, student use of resources such as videos, lecture slides and wiki articles for exam preparation has been investigated [13] as has VLE data to understand online revision activities of a small group of students [14]. Papamitsiou & Economides also touch upon exam revision, noting in their conclusion that goalon this assumption, goal-expectancy affects how students perform in their assessment.…”

Section: Introductionmentioning

confidence: 99%

Does the Quality and Quantity of Exam Revision Impact on Student Satisfaction and Performance in the Exam Itself?: Perspectives From Undergraduate Distance Learners

Cross¹,

Whitelock²,

Mittelmeier³

2016

EDULEARN Proceedings

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This paper reports the findings of a large-scale survey in to the student experience of assessment at the UK's largest distance learning university. Three key aspects of assessment were covered in the survey: formative assessment, revision for examination and the examination/end of module; with a view of providing insight for more effective learning designers and learning analytics. This analysis meets an urgent need to better understand the assessment analytics associated with the 'revision' periodthe weeks leading up to an examination that may be crucial in ensuring student assessment success, building confidence, and improving progression to the next course. Using results from an online questionnaire (n=281) sent to undergraduate distance learners and follow-up telephone interviews (n=13), this paper will examine some of the relationships between the revision and examination experience. Specific regard will be paid to usefulness of revision resources, time spent revising, enjoyment, reflection and learning, exam preparedness and clarity, mark satisfaction and score received. The paper will begin with an overview of the central findings of the survey, followed by a focus on the relationship between the 'revision' period and the examination itself. In particular, aspects of student experience, performance and self-reported learning effort will be explored. The research represents an important step in extending the scope of assessment analytics and in better understanding the opportunities for providing more timely, targeted or personalised learning support. Key findings of the analysis reported are that revising for an exam and the exam itself are relatively distinct experiences; there is no significant correlation between time spent revising, usefulness of revision resources and module exam score. Similarly, revision for learning, revision design and satisfaction with revision resources appear as distinct factors in the student experience. These results have clear implications for the design and teaching of assessment.

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“…As observed by Hecking, Ziebarth and Hoppe (2014) in their study of resourcebased learning in an online module, it appears that participant/resource interactions cluster into different patterns. Note the algorithm used to lay the networks out (Clauset, Newman and Moore 2004) is deliberately designed to emphasize similarity and difference; the separate clusters of participants in each panel of the figure do not use exclusively different resources.…”

Section: Figure 1 About Herementioning

confidence: 72%

Learning analytics: challenges and limitations

Wilson

Watson

Thompson

et al. 2017

Teaching in Higher Education

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Learning analytic implementations are increasingly being included in learning management systems in higher education. We lay out some concerns with the way learning analytics – both data and algorithms – are often presented within an unproblematized Big Data discourse. We describe some potential problems with the often implicit assumptions about learning and learners – and indeed the tendency not to theorize learning explicitly – that underpin such implementations. Finally, we describe an attempt to devise our own analytics, grounded in a sociomaterial conception of learning. We use the data obtained to suggest that the relationships between learning and the digital traces left by participants in online learning are far from trivial, and that any analytics that relies on these as proxies for learning tends towards a behaviorist evaluation of learning processes

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Analysis of Dynamic Resource Access Patterns in Online Courses

Cited by 29 publications

References 28 publications

Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

Does the Quality and Quantity of Exam Revision Impact on Student Satisfaction and Performance in the Exam Itself?: Perspectives From Undergraduate Distance Learners

Learning analytics: challenges and limitations

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