Programming Massive Open Online Courses Research Articles

Predicting Dropout in Programming MOOCs through Demographic Insights

Massive Open Online Courses (MOOCs) have gained widespread popularity for their potential to offer education to an unlimited global audience. However, they also face a critical challenge in the form of high dropout rates. This paper addresses the need to identify students at risk of dropping out early in MOOCs, enabling course organizers to provide targeted support or adapt the course content to meet students’ expectations. In this context, zero-time dropout predictors, which utilize demographic data before the course commences, hold significant potential. Despite a lack of consensus in the existing literature regarding the efficacy of demographic data in dropout prediction, this study delves into this issue to contribute new insights to the ongoing discourse. Through an extensive review of prior research and a detailed analysis of data acquired from two programming MOOCs, we aim to shed light on the relationship between students’ demographic characteristics and their likelihood of early dropout from MOOCs, using logistic regression. This research extends the current understanding of the impact of demographic features on student retention. The results indicate that age, education level, student status, nationality, and disability can be used as predictors of dropout rate, though not in every course. The findings presented here are expected to affect the development of more effective strategies for reducing MOOC dropout rates, ultimately enhancing the educational experience for online learners.

To Schedule or not to Schedule: The Effects of Course Structure on Programming MOOC Performance

Massive Open Online Courses (MOOCs) have become hugely popular recently. MOOCs can offer high-quality education for anyone interested and equalize the whole education field. Still, there are different methodologies for running MOOCs. Coming up with the most suitable methodology benefits both students and teachers. In this study, we have limited the methodological focus to observing scheduled and unscheduled instances of similar MOOC courses. While unscheduled MOOC courses can provide flexibility, they also require self-regulated learning strategies for students to succeed. To observe this, we compare the effectiveness of scheduled and unscheduled programming MOOC courses to find the most effective methodology. For this, we compare the pass rates and grade averages of five instances (two unscheduled and three scheduled) of Python and Java programming MOOCs. The results show that while the attendance numbers are higher in the unscheduled versions, in the scheduled instances the pass rate is significantly better, and students’ progression is much swifter. It also seems that the higher proportion of university students enrolled in a MOOC course positively affects the retention rate. Moreover, the students in the recent unscheduled Python version seem to score significantly higher grades than in its scheduled counterpart. Based on our experiments, the scheduled and unscheduled versions complement each other. Hence, we suggest that, whenever feasible, the maximal benefits would be gained if both types of MOOCs are run simultaneously.

The mysterious adventures of Detective Duke: How storified programming MOOCs support learners in achieving their learning goals

About 15 years ago, the first Massive Open Online Courses (MOOCs) appeared and revolutionized online education with more interactive and engaging course designs. Yet, keeping learners motivated and ensuring high satisfaction is one of the challenges today's course designers face. Therefore, many MOOC providers employed gamification elements that only boost extrinsic motivation briefly and are limited to platform support. In this article, we introduce and evaluate a gameful learning design we used in several iterations on computer science education courses. For each of the courses on the fundamentals of the Java programming language, we developed a self-contained, continuous story that accompanies learners through their learning journey and helps visualize key concepts. Furthermore, we share our approach to creating the surrounding story in our MOOCs and provide a guideline for educators to develop their own stories. Our data and the long-term evaluation spanning over four Java courses between 2017 and 2021 indicates the openness of learners toward storified programming courses in general and highlights those elements that had the highest impact. While only a few learners did not like the story at all, most learners consumed the additional story elements we provided. However, learners' interest in influencing the story through majority voting was negligible and did not show a considerable positive impact, so we continued with a fixed story instead. We did not find evidence that learners just participated in the narrative because they worked on all materials. Instead, for 10–16% of learners, the story was their main course motivation. We also investigated differences in the presentation format and concluded that several longer audio-book style videos were most preferred by learners in comparison to animated videos or different textual formats. Surprisingly, the availability of a coherent story embedding examples and providing a context for the practical programming exercises also led to a slightly higher ranking in the perceived quality of the learning material (by 4%). With our research in the context of storified MOOCs, we advance gameful learning designs, foster learner engagement and satisfaction in online courses, and help educators ease knowledge transfer for their learners.

Measuring the Post-Impact of Programming MOOCs: Development and Validation of an Instrument

Massive open online courses (MOOCs) can potentially affect the lives of millions of people, but there is little research on how a programming MOOC could affect participants’ lives after participation. In Estonia, we have organised several programming MOOCs over the years, attended by over 14,000 people. This inspired us to develop and validate a scale for measuring the impact of programming MOOC on participants’ lives. We analysed data from 1261 MOOC participants who had completed at least one of our programming MOOCs. Confirmatory factor analysis (CFA) was used to validate the developed scale. The final model fitted quite well to the data. We found that the factors that influence learners’ lives after a MOOC include acquired learning skills, interest in continuing computer science related studies, learning experience gained from the MOOC, the MOOC’s impact on work and personal life, and new contacts that are established with other participants and organisers. We also compared the means of the factors. The highest-rated factor was related to the learning experience from the MOOC and the lowest-rated was related to finding new contacts through the MOOC.

Dropout Time and Learners’ Performance in Computer Programming MOOCs

One of the problems regarding MOOCs (Massive Open Online Courses) is the high dropout rate. Although dropout periods have been studied, there is still a lack of understanding of how dropout differs for MOOCs with different levels of difficulty. A quantitative study was conducted to determine the periods with the highest dropouts in computer programming MOOCs and the performance of the dropouts on the course before dropping out. Four occurrences of three MOOCs, with different durations, difficulty of the topic, and the degree of supportive methods, were included. The results showed that dropout was highest at the beginning of all studied courses. Learners also dropped out before the project. In the easier and shorter courses, most dropouts were successful until they quit the course. In longer and more difficult courses, learners mainly dropped out in the week they started due to experiencing problems with the course activities. It is suggested to recommend that learners take courses at a level that suits them if their current course is too easy or difficult and encourage learners to use course resources for help. It would be a good idea to provide learners with example topics to assist them in starting with a project.

Using image recognition to automatically assess programming tasks with graphical output

There are thousands of participants in different programming MOOCs (Massive Open Online Courses) which means thousands of solutions have to be assessed. As it is very time-consuming to assess that amount of solutions manually, using automated assessment is essential. Since task requirements must be strict for the solutions to be automatically gradable, it often limits the types of different assignments and creativity. In order to promote more creativity we wanted to enable programming tasks with graphical output. In order to analyze and assess the creative tasks we developed, implemented and analyzed a system capable of assessing the graphical output of a solution program using image recognition. Image recognition is used to analyze the graphical output (image) produced by the solution program. The graphical output with a keyword attached to it is sent to an image recognition service provider that responds with a probability score. The solution is accepted or rejected based on the probability of a given object appearing in the image. The system was tested and evaluated in two runs of the MOOC “Introduction to Programming.” In the first run, we used the system to automatically assess the solutions of programming tasks on a predefined topic and in the second run on a topic chosen by the participant. The evaluation of the usefulness of the system and overview of participants’ feedback are presented as results. Suggestions for future improvements of the system and possible research are also listed.

Troubleshooters for Tasks of Introductory Programming MOOCs

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%.

What motivates enrolment in programming MOOCs?

AbstractLearners 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.

Developing a Computer Programming MOOC

Abstract Open Learning movement opens up opportunities for the collaboration between institutions, educators and learners globally, and for enabling more meaningful engagement in teaching and learning. Massive Open Online Courses (MOOCs), one of the latest internet revolutions, boasting tens or hundreds of thousands of participants worldwide, and, hence, present an intense interest by the research and education community. Particularly, there is a strong interest in investigating the added value of collaborative features for enriching the development process, as well as the quality of the result. The purpose of this research is to explore the perspectives of a team-based methodology for the implementation of MOOCs. In this paper we present a methodology for a team-based development of MOOCs, illustrating the development process of a Computer Programming MOOC.