Participants and completers in programming MOOCs

Luik, Piret; Feklistova, Lidia; Lepp, Marina; Tõnisson, Eno; Suviste, Reelika; Gaiduk, Maria; Säde, Merilin; Palts, Tauno

doi:10.1007/s10639-019-09954-8

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…We infer that this result is related to the better prior knowledge of this group. Undoubtedly, learners' prior knowledge can affect the effectiveness of the time investment in learning, which is a factor that MOOC teachers need to consider (Greene et al, 2015;Luik et al, 2019). In addition, we found that the overall learning performance for the NET groups was not as good as that of the ET group, even for the NET1 group, which ranked third in time investment and management.…”

Section: Learning Performance Resultsmentioning

confidence: 77%

MOOC learners' time‐investment patterns and temporal‐learning characteristics

Wang

et al. 2021

Computer Assisted Learning

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Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course design and teaching. However, understanding of how learners invest and manage their time during online learning remains limited.Objectives: This study aims to discover the typical time-investment patterns of MOOC learners and their temporal-learning characteristics based on a systematic time-investment analysis framework and their relationship with learning performance.Methods: Based on a proposed time-investment-analysis framework, this study applied statistical, cluster and lag sequential analyses to investigate learners' timeinvestment patterns and their relationships with learning performance, session time allocation, and learning sequences by analysing the learning data from 12,463 participants of a Massive Open Online Course (MOOC) in China. Results and Conclusions:Seven time-investment patterns of MOOC learners were defined, and learning performance was found to differ among them. Further analysis shows that high performers invested time throughout the whole course and allocated time to multiple activities, exam-takers performed better in time management and produced more behavioural sequences related to cognitive strategy and recourse use, and learners' motivation and prior knowledge affected the management and effectiveness of their time investment. Implications:The results support the recognition and evaluation of online learning time-investment patterns and suggest relevant cues for improving MOOC design and teaching.

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Section: Learning Performance Resultsmentioning

confidence: 77%

MOOC learners' time‐investment patterns and temporal‐learning characteristics

Wang

et al. 2021

Computer Assisted Learning

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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%

Learners’ Performance in a MOOC on Programming

2021

Self Cite

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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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“…Participants of MOOCs had been characterized predominantly from development countries as young, well-educated and full-time working professionals looking to learn new skill set for their career's advancement (Luik, 2019). Educators widely used MOOCs as means of their professional development to learn more about the subjects that they are teaching (Netwon, 2015).…”

Section: Participants Of Moocsmentioning

confidence: 99%

Capacities and Obstacles of Kuwait Medical Educational Sector in Transitioning Education System to Online Form: A Paradigm Shift

AlDousari¹

2021

IJBM

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Background: During this coronavirus outbreak, online teaching and learning have become highly common in medical education. The majority of students and professors have transitioned to this comparatively modern online learning model. While faculty members found the transition to online education to be overwhelming, they persevered. Aim: study aims to investigate the Kuwait Medical educational sector's capacities and obstacles in transitioning the education system to the online form. Method: This study used a close-ended survey questionnaire constructed on Google Forms and distributed to the participants via e-mail. The study used an online Google form for formulating the questionnaire survey, which was later sent to the participants via e-mail. Results: The results showed that most of the faculty members (53%) were satisfied with online learning. The most prominent difficulty emphasized by the faculty members (35%) was network vulnerability n online teaching. The majority of the faculty members (45%) agreed that transitioning to remote teaching is a big challenge and that there is inequality for different technology uses (56%). Conclusion: Online learning has been increasingly relevant in medical education in recent months. It has its own set of benefits for improving students' knowledge and skills, and it may be used as an instructional tool in medical school.

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Participants and completers in programming MOOCs

Cited by 14 publications

References 24 publications

MOOC learners' time‐investment patterns and temporal‐learning characteristics

MOOC learners' time‐investment patterns and temporal‐learning characteristics

Learners’ Performance in a MOOC on Programming

Capacities and Obstacles of Kuwait Medical Educational Sector in Transitioning Education System to Online Form: A Paradigm Shift

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