Completers’ Engagement Clusters in Programming Mooc: The Case of Estonia

Feklistova, Lidia; Lepp, Marina; Luik, Piret

doi:10.21125/iceri.2019.0341

Cited by 2 publications

(7 citation statements)

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“…Previous experience with programming was not required. Course participants and completers differed in terms of demographic and social background [46], motivation [47], interaction with provided activities [16], and resolved difficulties in programming tasks in different ways [17].…”

Section: Context Of Studymentioning

confidence: 99%

“…The description of the study sample is provided in Table 1. Previously, we revealed completers' clusters based on their reported engagement [16] and difficulty-resolving strategies [17]. Those clusters were formed using K-mean cluster analysis.…”

Section: Samplementioning

confidence: 99%

“…Each learner who completed the course was assigned to one of the completers' clusters identified in previous studies about completers' engagement [16] and difficulty-resolving [17]. This manipulation was possible because the current study used the same dataset with the same sample, and the data about completers' cluster membership were stored.…”

Section: Data Collectionmentioning

confidence: 99%

“…The paper tried to shed light on those aspects in the context of a MOOC on programming. In this study, descriptive statistics and non-parametric tests are employed to examine performance in programming tasks and quizzes by non-completers and completers, as well as by previously identified completers with different engagement styles [16] and difficulty-resolving patterns [17].…”

Section: Introductionmentioning

confidence: 99%

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Learners’ Performance in a MOOC on Programming

2021

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In every course, there are learners who successfully pass assessments and complete the course. However, there are also those who fail the course for various reasons. One of such reasons may be related to success in assessment. Although performance in assessments has been studied before, there is a lack of knowledge on the degree of variance between different types of learners in terms of scores and the number of resubmissions. In the paper, we analyse the performance in assessments demonstrated by non-completers and completers and by completers with different engagement levels and difficulty-resolving patterns. The data have been gathered from the Moodle statistics source based on the performance of 1065 participants, as regards their completion status, the number of attempts made per each programming task and quiz, and the score received per quiz. Quantitative analysis was performed with descriptive statistics and non-parametric tests. Non-completers and completers were similar in resubmissions per quiz, but the former, expectedly, made more resubmissions per programming task and received lower quiz scores. Completers made more attempts per task than per quiz. They could provide a correct solution with a few resubmissions and receive good scores already at a pragmatic engagement level. At the same time, the increased use of help sources in case of difficulties was also associated with a higher number of attempts and lower quiz scores received. The study may have implications in understanding the role of assessments in dropouts and how completers with different engagement and difficulty-resolving patterns cope with assessments.

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Section: Context Of Studymentioning

confidence: 99%

Section: Samplementioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learners’ Performance in a MOOC on Programming

2021

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“…Existing research on MOOCs has focused on classification of MOOC learners based on their behavior during the course (Feklistova et al, 2019;Kahan et al, 2017;Kizilcec et al, 2013;Tseng et al, 2016). However, different motivational goals may predict different behavioral patterns for MOOC learners and dropouts (Kizilcec & Schneider, 2015), and MOOC completers can be characterized according to their motivation to learn (Barak et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?

Luik

Lepp

2021

IRRODL

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Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to compare how these clusters differed in terms of intention to complete and actual completion rate. The sample consisted of 1,181 respondents to the pre-course questionnaire in the Introduction to Programming MOOC. A validated motivation scale, based on expectancy-value theory and k-means cluster analysis, was used to form the groups. The four identified clusters were named as Opportunity motivated (27.7%), Over-motivated (28.6%), Success motivated (19.6%) and Interest motivated (24.0%). Comparison tests and chi-square test were used to describe the differences among the clusters. There were statistically significant differences among clusters in self-evaluated probability of completion. Also, significant differences emerged among three clusters in terms of percentages of respondents who completed the MOOC. Interestingly, the completion rate was the lowest in the Over-motivated cluster. A statistically significant higher ratio of completers to non-completers was found in the Opportunity motivated, Success motivated, and Interest motivated clusters. Our findings can be useful for MOOC instructors, as a better vision of participants’ motivational profiles at the beginning of the MOOC might help to inform the MOOC design to better support different needs, potentially resulting in lower dropout rates.

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Completers’ Engagement Clusters in Programming Mooc: The Case of Estonia

Cited by 2 publications

References 0 publications

Learners’ Performance in a MOOC on Programming

Learners’ Performance in a MOOC on Programming

Are Highly Motivated Learners More Likely to Complete a Computer Programming MOOC?

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