Motivation as a Lens to Understand Online Learners

Kizilcec, René F.; Schneider, Emily

doi:10.1145/2699735

Cited by 199 publications

(83 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Rooij and Zirkle (2016) and Nagashima (2014) also refer to interactivity, that is, the connections among all MOOC participants, as an enhancer of student engagement. Furthermore, this factor's effect can be increased by knowing other learners in the MOOC, who can promote engagement with the course resources (Kizilcec & Schneider, 2015). Therefore, 'Acquaintances' was another success factor presented in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

MOOC Success Factors: Proposal of an Analysis Framework

Azevedo

Marques

2017

JITE:IIP

View full text Add to dashboard Cite

Aim/PurposeFrom an idea of lifelong-learning-for-all to a phenomenon affecting higher education, Massive Open Online Courses (MOOCs) can be the next step to a truly universal education. Indeed, MOOC enrolment rates can be astoundingly high; still, their completion rates are frequently disappointingly low. Nevertheless, as courses, the participants' enrolment and learning within the MOOCs must be considered when assessing their success. In this paper, the authors' aim is to reflect on what makes a MOOC successful to propose an analysis framework of MOOC success factors. Background A literature review was conducted to identify reported MOOC success factors and to propose an analysis framework. Methodology This literature-based framework was tested against data of a specific MOOC and refined, within a qualitative interpretivist methodology. The data were collected from the 'As alterações climáticas nos média escolares -Clima@EduMedia' course, which was developed by the project Clima@EduMedia and was submitted to content analysis. This MOOC aimed to support science and school media teachers in the use of media to teach climate change. Contribution By proposing a MOOC success factors framework the authors are attempting to contribute to fill in a literature gap regarding what concerns criteria to consider a specific MOOC successful. Findings This work major finding is a literature-based and empirically refined MOOC success factors analysis framework. Recommendations for PractitionersThe proposed framework is also a set of best practices relevant to MOOC developers, particularly when targeting teachers as potential participants. Recommendation for ResearchersThis work's relevance is also based on its contribution to increasing empirical research on MOOCs. MOOC Success Factors 234 Impact on SocietyBy providing a proposal of a framework on success factors for MOOCs, the authors hope to contribute to the quality of MOOCs. Future ResearchFuture work should refine further the proposed framework, by testing it against data collected in other MOOCs.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

MOOC Success Factors: Proposal of an Analysis Framework

Azevedo

Marques

2017

JITE:IIP

View full text Add to dashboard Cite

show abstract

“…However, they do not report findings specifically for science or engineering courses. Kizilcec & Schneider (2015) studied learners' reported reasons for enrolling in MOOCs and found thirteen different reasons, varying from general interest to improving English. The researchers then turned the open-ended questions into a survey question, asking respondents to select "all that apply" on a different sample.…”

Section: Literature Reviewmentioning

confidence: 99%

“…MOOC learners generally enter the course with a wide variety of participation objectives (Kizilcec & Schneider, 2015) making them far too heterogeneous to fully characterize simply by the percentage using course resources. These latent or hidden variables contribute to learner behaviour in the course and must be explored (Thille et al, 2014).…”

Section: Learner Usagementioning

confidence: 99%

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

“…First, we analyze responses to the aforementioned question about student rationales for signing up for an online course. Analyzing these free response items with STM allows educators and researchers to examine how students articulate their motivations in their own words, and then evaluate quantitatively how student motivations correlate with demographic characteristics or course-taking behaviour (Kizilcec & Schneider, 2015). In this first example, we provide a detailed walkthrough of the workflow for using the STM package and analyzing the resulting output.…”

Section: Overviewmentioning

confidence: 99%

Computer-Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses

et al. 2014

View full text Add to dashboard Cite

ABSTRACT:Dealing with the vast quantities of text that students generate in Massive Open Online Courses (MOOCs) and other large-scale online learning environments is a daunting challenge. Computational tools are needed to help instructional teams uncover themes and patterns as students write in forums, assignments, and surveys. This paper introduces to the learning analytics community the Structural Topic Model, an approach to language processing that can 1) find syntactic patterns with semantic meaning in unstructured text, 2) identify variation in those patterns across covariates, and 3) uncover archetypal texts that exemplify the documents within a topical pattern. We show examples of computationally aided discovery and reading in three MOOC settings: mapping students' self-reported motivations, identifying themes in discussion forums, and uncovering patterns of feedback in course evaluations.

show abstract

Motivation as a Lens to Understand Online Learners

Cited by 199 publications

References 41 publications

MOOC Success Factors: Proposal of an Analysis Framework

MOOC Success Factors: Proposal of an Analysis Framework

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

Computer-Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses

Contact Info

Product

Resources

About