Learners who enrol in massive open online courses (MOOCs) have different backgrounds and tend to have different motivations than learners in traditional courses. Based on value-expectancy theory, an instrument was developed to measure motivation for enrolling in a programming MOOC. A study with 1229 adult participants in Estonian-language programming course "About Programming" was conducted to validate the instrument. Results of confirmatory factor analysis validated the 7-factor scale named factors influencing enrolment in MOOC (FIEM). FIEM comprises three factors of expectancies, three factors of values and one factor of social influence. The highest and lowest rated motivational factors influencing enrolment in programming MOOC are discussed in the paper. Interest in and expectations for the course, personal suitability of distance learning and suitability for family and work are the highest-rated motivational factors for those who enrol in MOOC. Usefulness related to own children, social influence and usefulness to related to certification were the lowest rated. The results of this study can be useful for designers of programming MOOCs and the developed scale might be used in future studies.
Learning programming has become more and more popular and organizing introductory massive open online courses (MOOCs) on programming can be one way to bring this education to the masses. While programming MOOCs usually use automated assessment to give feedback on the submitted code, the lack of understanding of certain aspects of the tasks and feedback given by the automated assessment system can be one persistent problem for many participants. This paper introduces troubleshooters, which are help systems, structured like decision trees, for giving hints and examples of certain aspects of the course tasks. The goal of this paper is to give an overview of usability (benefits and dangers) of, and the participants’ feedback on, using troubleshooters. Troubleshooters have been used from the year 2016 in two different programming MOOCs for adults in Estonia. These MOOCs are characterized by high completion rates (50–70%), which is unusual for MOOCs. Data is gathered from the learning analytics integrated into the troubleshooters’ environment, letters from the participants, questionnaires, and tasks conducted through the courses. As it was not compulsory to use troubleshooters, the results indicate that only 19.8% of the users did not use troubleshooters at all and 10% of the participants did not find troubleshooters helpful at all. The main difference that appeared is that the number of questions asked from the organizers about the programming tasks during the courses via helpdesk declined about 29%.
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.
Education worldwide was affected by the coronavirus pandemic when many countries, including Estonia, had to switch to distance learning. It was an unexpected change in education and required a response from relevant stakeholders. This study aims to understand the activities of different stakeholders as revealed in the messages of the Facebook group ‘Homeschooling with technology’ from 6 March to 26 April 2020. A mixed method study design was used, including quantitative and qualitative content analysis of 872 messages posted by members of the Facebook group, which were divided into eight role groups. Teachers, educational technologists, principals and parents represented local stakeholders while external stakeholders included members from government institutions, supporters, teacher educators and members with other roles. The analysis covered activeness of each role group, emotional expressions, speech acts and topics represented in messages. The results indicate that educational technologists played a key role in handling the coronavirus pandemic situation in education. However, local stakeholders also received support from external stakeholders. The results help capture the roles, experiences and views of different stakeholders during the educational change caused by the coronavirus pandemic in order to learn from this and to be prepared for such situations in the future.
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