How to design for persistence and retention in MOOCs?

Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme. In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context

Ripple: Concept-Based Interpretation for Raw Time Series Models in Education

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extracting these features is labor-intensive for humans and computers. In this paper, we propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy with raw time series clickstreams in comparison to hand-crafted features. Furthermore, we extend concept activation vectors for interpretability in raw time series models. We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. Our experimental analysis on 23 MOOCs with millions of combined interactions over six behavioral dimensions show that models designed with our approach can (i) beat state-of-the-art educational time series baselines with no feature extraction and (ii) provide interpretable insights for personalized interventions. Source code: https://github.com/epfl-ml4ed/ripple/.Comment: Accepted as a full paper at AAAI 2023: 37th AAAI Conference on Artificial Intelligence (EAAI: AI for Education Special Track), 7-14 of February 2023, Washington DC, US

The Usefulness of a Massive Open Online Course about Postural and Technological Adaptations to Enhance Academic Performance and Empathy in Health Sciences Undergraduates

Massive open online courses (MOOCs) provide accessible and engaging information for Physical Therapy and Occupational Therapy students. The objective of this research was to determine the usefulness in improving academic performance and empathy in health sciences undergraduates, and to test a hypothetical model through structural equation analysis. This research was carried out using a descriptive and quasi-experimental design. It was conducted in a sample of 381 participants: 176 used a MOOC and 205 did not. The results of the Student’s t-test showed statistically significant differences in academic performance between the groups in favor of those students who had realized the MOOC. Participants carried out an evaluation rubric after taking MOOC. Statistically significant differences in empathy were also obtained between the pre (X = 62.06; SD = 4.41) and post (X = 73.77; SD = 9.93) tests. The hypothetical model tested via structural equation modeling was supported by the results. Motivation for the MOOC explained 50% of the variance. The MOOC (participation and realization) explained 58% of academic performance, 35% of cognitive empathy and 48% of affective empathy. The results suggest an association between higher realization and participation in a MOOC and higher levels of academic performance, and cognitive and affective empathy.University of Malaga (UMA) for the Call for Educational Innovation Projects (PIE19-148), to the Call 2019-2021Initiation Grant for Research from the UMA's Own Plan, in Modality A (Grade

MOOCs: The Factors Impacting Learners’ Continuance Intention, the Intention to Complete or Cancel a Course

The growing popularity of massive open online courses (MOOCs), especially during the COVID-19 pandemic, has attracted significant attention from researchers and businesses. Though many studies have investigated what motivates learners’ continuance intention, it is no less important to reveal the factors that lead to course completion or cancellation. The aim of this study is to reveal the factors impacting three different e-learning behaviour intentions– continuance intention, the intention to complete, and the intention to cancel MOOCs – by applying the theory of planned behaviour (TPB) and the technology acceptance model (TAM). Based on a survey of 299 respondents, it was revealed that the TAM only explains continuance intention but cannot be fully employed to predict two other e-learning behavior intentions. Also, participants’ support and self-efficacy, being a part of the TPB model, had an influence on the intention to complete the course, while they did not affect continuance intention. Only participants’ support had a moderate positive impact on the intention to cancel it. Moreover, it was revealed that continuance intention positively impacted the intention to complete and negatively impacted the intention to cancel the course. This expands the body of knowledge about learners’ motivations for three different e-learning behaviour intentions and has managerial implications for their development in emerging economies

Revealing the hidden patterns : a comparative study on profiling subpopulations of MOOC students.

Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex “big data” from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research, looking mainly into overall behavior, may have missed patterns related to student diversity. Using a large dataset from a MOOC offered by FutureLearn, we delve into a new way of investigating hidden patterns through both machine learning and statistical modelling. In this paper, we report on clustering analysis of student activities and comparative analysis on both behavioral patterns and demographical patterns between student subpopulations in the MOOC. Our approach allows for a deeper understanding of how MOOC students behave and achieve. Our findings may be used to design adaptive strategies towards an enhanced MOOC experience

Exploring how student motivation relates to acceptance and participation in MOOCs

In recent years, MOOCs have become firmly established as valid e-learning environments and, as such, have been developed by many universities using different types of platform. Given the voluntary nature of MOOC enrolment, motivation is crucial to our understanding of why students register for and complete these courses. The present study explores the motivations that characterize MOOC participants and how they relate to technology acceptance variables (data collected via questionnaires) and participation variables (observational data collected via the platform). Our results indicate that students show exceptionally high levels of intrinsic motivation. However, extrinsic motivation also plays a relevant role, suggesting that the two are not mutually exclusive. Although only intrinsic motivation appears to be systematically associated with differences in technology acceptance, both are associated with differences in participation, but in contrasting ways. Our results provide insights that will enable us to improve MOOC design in order to enhance participant satisfaction, particularly when different sources of motivation are involved. Future research based on the modeling of technology acceptance and participation will also benefit from this study

Developing sustainable business models for institutions’ provision of open educational resources: Learning from OpenLearn users’ motivations and experiences

Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OER’s value in generating revenue and detractors of OER have questioned whether they are in competition with formal education. This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open University’s (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learners’ performance and self-reliance, leading to increased retention and satisfaction with the learning experience