Predicting Student Success in a Self-Paced Mathematics MOOC

abstract: While predicting completion in Massive Open Online Courses (MOOCs) has been an active area of research in recent years, predicting completion in self-paced MOOCS, the fastest growing segment of open online courses, has largely been ignored. Using learning analytics and educational data mining techniques, this study examined data generated by over 4,600 individuals working in a self-paced, open enrollment college algebra MOOC over a period of eight months. Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed). The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.Dissertation/ThesisDoctoral Dissertation Educational Technology 201

Who Continues in a Series of Lifelong Learning Courses?

Publisher Copyright: © 2022 ACM.Although computing education research quite often targets within-university courses, an important role of universities is educating the public through open online lifelong learning offerings. Compared to within-university courses, in lifelong learning, the student population is often more diverse. For example, participants often have more varied motivations and aspirations as well as more varied educational backgrounds. In this work, we explore what kinds of learners attend open online lifelong learning programming courses and what characteristics of learners lead to completing courses and proceeding to subsequent courses. We examine student-related factors collected through surveys in our online course environment. These factors include motivation, previous experience, and demographics. Our results show that motivations, previous experience, and demographics by themselves only explain a small amount of the variance in completing courses or continuing to a subsequent course. At the same time, we identify individual factors that are more likely to lead to learners dropping out (or continuing) in the courses. Our study provides further evidence that lifelong learning benefits most the already educated part of the population with prior knowledge and high motivation. This calls for further studies that seek to identify means to engage and support participants less likely to continue in such courses.Peer reviewe

Predicting Learners' Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

Proceeding of: 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018.In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners' success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners' self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners: (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek for the information required to pass assessments. For both type of learners, we found a group of variables as the most predictive: (1) the self-reported SRL strategies 'goal setting', 'strategic planning', 'elaboration' and 'help seeking'; (2) the activity sequences patterns 'only assessment', 'complete a video-lecture and try an assessment', 'explore the content' and 'try an assessment followed by a video-lecture'; and (3) learners' prior experience, together with the self-reported interest in course assessments, and the number of active days and time spent in the platform. These results show how to predict with more accuracy when students reach a certain status taking in to consideration not only low-level data, but complex data such as their SRL strategies.This work was supported by FONDECYT (Chile) under project initiation grant No.11150231, the MOOC-Maker Project (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), the LALA Project (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and CONICYT/DOCTORADO NACIONAL 2016/21160081, the Spanish Ministry of Education, Culture and Sport, under an FPU fellowship (FPU016/00526) and the Spanish Ministry of Economy and Competiveness (Smartlet project, grant number TIN2017-85179-C3-1-R) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).Publicad

Influence of employer support for professional development on MOOCs enrolment and completion: Results from a cross-course survey

Although the potential of open education and MOOCs for professional development is usually recognized, it has not yet been explored extensively. How far employers support non-formal learning is still an open question. This paper presents the findings of a survey-based study which focuses on the influence of employer support for (general) professional development on employees’ use of MOOCs. Findings show that employers are usually unaware that their employees are participating in MOOCs. In addition, employer support for general professional development is positively associated with employees completing MOOCs and obtaining certificates for them. However, the relationship between employer support and MOOC enrollment is less clear: workers who have more support from their employers tend to enroll in either a low or a high number of MOOCs. Finally, the promotion of a minimum of ICT skills by employers is shown to be an effective way of encouraging employee participation in the open education ecosystem.JRC.J.3-Information Societ

The role of gender and employment status in MOOC learning: An exploratory study

Background Learners in a given massive open online course (MOOC) are usually provided with the same learning materials, guided by the same syllabus, and assessed in the same format. This “one-size-fits-all” approach constrains learners' ability to reap the optimal benefits from online learning. Objectives This study aims to characterize learners' differences in MOOC learning. Specifically, it examines how learners might vary in their enrolment motivation and the development of continuance intention to learn in a MOOC because of their gender and employment status. Methods Data were collected via a questionnaire survey. Quantitative and qualitative methods were used to analyse data from 664 learners in a Chinese MOOC. Results and Conclusion The research revealed significant differences in learners' enrolment motivation across groups defined by employment status, but not for gender groups. Learner groups defined by gender and employment status experienced variant psychological processes when deciding to continue to learn in the MOOC. Major Takeaways Working adults stressed the instrumental values derived from MOOC learning; therefore, it is vital to design and integrate additional features into the MOOC to satisfy their needs. Besides, it would be critical to understand female learners' and working adults' expectations of MOOC learning, as they are more sensitive to confirmation in determining their attitudes toward learning in a MOOC. A short pre-course survey of learners' expectations would serve the purpose

An Experimental Study of Self-regulated Learning Strategies Application in Moocs

Online learning has been widely adopted in higher education to reach students who typically would not have a chance to complete accredited courses (Kentnor, 2015). Massive open online courses (MOOC), which is a type of online learning, makes it easier for people to take university courses with internet access and a fraction of cost compared to traditional residential programs (Reich, 2020). MOOCs also become popular for those who want to increase their professional profile or advance their academic career (Pheatt, 2017). However, online learning has long been criticized for its universally low completion rates, high dropout rate and poor learning performance (Almeda et al., 2018). This phenomenon is more exacerbated in MOOC environments. Historical studies have attempted to support learner self-regulated learning (SRL) activities in order to enhance completion rates and academic outcomes. Prior studies have conducted pre-course questionnaires as inexpensive SRL interventions to prompt learners as SRL support(Kizilcec et al., 2017, Kizilcec & Cohen, 2017; Kizilcec et al., 2020; Yeomans & Reich, 2017). Yet, these one-time-only, short-term interventions only yield limited or no effects. This study implemented and evaluated the effectiveness of an alternative intervention, the self-regulated learning user interface (SRLUI), to support students' self-regulated learning (SRL) strategies in a MOOC environment. SRLUI is based on Zimmerman’s (2000) SRL model and develops learner’s SRL skills through longitudinal, recurring practice of multiple SRL dimensions activities (i.e., goal setting, self-evaluation, task planning, setting reminders) with content-specific information. The study utilized a randomized experimental design and implemented SRLUI in eight MOOCs with a total of 808 participants. The results indicated a higher usage rate of SRL support compared to the historical findings, which may be owing to the SRL support embedded into the learning activities throughout the course. Also, the study showed improved learning outcomes for a subgroup of participants, but there was no reduction in the number of dropouts. Based on the findings of this study, it is recommended that a personalized SRL tool featuring content-specific information should be embedded in online courses. The research design also recorded direct cognitive records of learners' SRL activities, which yield stronger validity compared to trace and survey data. The result suggested SRLUI might only benefit a subgroup of learners with passing grades. Thus, it is recommended that future research identify various subgroups of learner profiles in MOOC environments and to consider how to reach and support learners in different subgroups