Student engagement in massive open online courses

Sinclair, Jane; Kalvala, Sara

doi:10.1504/ijlt.2016.079035

Cited by 20 publications

(19 citation statements)

References 26 publications

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“…Caulfield, Collier, and Halawa (2013) report limited participation in forums, and Milligan and Littlejohn (2014) found little exchange of ideas and experience in a MOOC on clinical trials. Sinclair and Kalvala (2016) suggest that online communities are claimed as a positive aspect of MOOCs, but that collaborative learning is underutilised. Since our findings suggest active engagement, this appears to be an aspect worthy of further investigation.…”

Section: Discussionmentioning

confidence: 99%

“…In many respects, these instruments have become the proxy for our concepts of engagement, as exemplified by the widely deployed North American National Survey of Student Engagement (NSSE) developed by Indiana University. Sinclair and Kalvala (2016) suggested that the NSSE focuses on behavioural level dimensions of student engagement, and although they are cautious about the interpretation of results from this and other surveys, they also cited the work by Pascarella, Seifert, and Blaich (2010) which suggested that engagement surveys can indicate learning gain. The UK Engagement Survey (UKES) used in this study draws on a 3 (page number not for citation purpose) subset of NSSE questions.…”

Section: Introductionmentioning

confidence: 99%

“…Drawing on data such as the number of video downloads and views, threads opened, comments submitted and exams completed, learners were categorised into seven types ranging from tasters (64.8%) to social engagers (0.6%). Whilst such categories provide some insight into the actions taken by learners, it should be acknowledged that on their own they do not reveal when activities have led to meaningful learning (Sinclair and Kalvala 2016).…”

Section: Introductionmentioning

confidence: 99%

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Understanding learner engagement on a blended course including a MOOC

Cornelius

Calder

Mtika

2019

Research in Learning Technology

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The use of Massive Open Online Courses (MOOCs) in blended learning contexts is becoming increasingly common, but relatively little is known about the experiences of on-campus learners taking MOOCs. This article reports research that explored the experiences of on-campus learners taking a blended course which included a MOOC. Use of the UK Engagement Survey provided a focus on engagement and permitted comparisons with a wider cohort of on-campus learners. Findings show that there were no differences between learners on the blended course and the wider cohort of on-campus learners for some aspects of engagement. However, learners on the blended course were more engaged than oncampus learners on specific aspects measured by the UKES survey including those which appear related to social learning. Evidence from a small number of interviews is used to explore issues raised, and informed by the Community of Inquiry framework, factors which influence blended learners' engagement with the MOOC are discussed. Some of the findings support the call for amendments to the community of inquiry framework for MOOC contexts and provide evidence of issues related to social and teaching presence that may need additional consideration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding learner engagement on a blended course including a MOOC

Cornelius

Calder

Mtika

2019

Research in Learning Technology

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“…is the vector representing the complete set of M units in the hidden layer h. {(L o )} U o=1 is the vector representing the U neurons in the output layer o. In the case of a single hidden layer, the collection of weights can be represented by two matrices {W 1 ij , W 2 kj } The weight matrix which connects the input to the hidden layer can be represented as W 1 ij , and the weight matrix that links the hidden to the output layer is W 2 kj . B i L i=1 is the column vector of biases for layer i.…”

Section: ) Neural Networkmentioning

confidence: 99%

“…MOOCs provide a variety of multimedia tools to deliver an interactive learning environment. MOOCs offer valuable digital learning resources, allowing students to access information from all over the world [2].…”

Section: Introductionmentioning

confidence: 99%

Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques

et al. 2019

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Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely at-risk student model and learning achievement model. The models have the potential to detect the students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in at-risk student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at-risk students. INDEX TERMS Machine learning, massive open online courses, receiver operator characteristics, area under curve.

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