A conceptual framework for learners self-directing their learning in MOOCs: components, enablers and inhibitors

The conceptual framework presented in this chapter describes the learning components influencing the learning experiences of adult informal learners engaged in MOOCs offered on the FutureLearn platform. It consists of five learning components: individual characteristics, technology, individual & social learning, organising learning, and context. These five learning components are driven by two enablers or inhibitors of learning: motivation and learning goals. For adult informal learners, motivation is mostly intrinsic, and learning goals are mostly personal. This research investigated the informal learning of 56 adult learners with prior online experience, as they studied various subjects in MOOCs. Literature on MOOCs, mobile and informal learning provides scientific support, in addition to literature clarifying the rationale for self-directed learning as a focus of investigation. The participants of this study voluntarily followed one of three FutureLearn courses that were rolled out for the first time at the end of 2014. Data were collected at three stages through an online survey (pre-course), self-reported learning logs (during the course), and semi-structured one-on-one interviews (post-course). The data were analysed using Charmaz’s (2014) method for constructing a grounded theory

Chrome Plug-in to Support SRL in MOOCs

Proceeding of: 6th European MOOCs Stakeholders Summit, EMOOCs 2019 Naples, Italy, May 20–22, 2019.Massive Open Online Courses (MOOCs) have gained popularity over the last years, offering a learning environment with new opportunities and challenges. These courses attract a heterogeneous set of participants who, due to the impossibility of personal tutorship in MOOCs, are required to create their own learning path and manage one’s own learning to achieve their goals. In other words, they should be able to self-regulate their learning. Self-regulated learning (SRL) has been widely explored in settings such as face-to-face or blended learning environments. Nevertheless, research on SRL in MOOCs is still scarce, especially on supporting interventions. In this sense, this document presents MOOCnager, a Chrome plug-in to help learners improve their SRL skills. Specifically, this work focuses on 3 areas: goal setting, time management and selfevaluation. Each area is included in one of the 3 phases composing Zimmerman’s SRL Cyclical Model. In this way, the plug-in aims to support enrolees’ self-regulation throughout their complete learning process. Finally, MOOCnager was uploaded to the Chrome Web Store, in order to get a preliminary evaluation with real participants from 6 edX Java MOOCs designed by the Universidad Carlos III de Madrid (UC3M). Results were not conclusive as the use of the plug-in by the participants was very low. However, learners seem to prefer a seamless tool, integrated in the MOOC platform, which is able to assist them without any learner-tool interaction.The authors acknowledge the eMadrid Network, funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. P2018/TCS-4307. This work also received partial support from the Spanish Ministry of Economy and Competitiveness/Ministry of Science, Innovation, and Universities, Projects RESET (TIN2014-53199-C3-1-R) and Smartlet (TIN2017- 85179-C3-1-R), and from the European Commission through Erasmus+ projects COMPETENSEA (574212-EPP-1-2016-1-NL-EPPKA2-CBHE-JP), LALA (586120-EPP-1-2017-1-ESEPPKA2- CBHE-JP), and InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP).Publicad

Learners Self-directing Learning in FutureLearn MOOCs: A Learner-Centered Study

This qualitative research study focuses on how experienced online learners self-direct their learning while engaging in a MOOC delivered on the FutureLearn platform. Self-directed learning is an important concept within informal learning and online learning. This study distinguishes itself from previous MOOC learner studies, by reporting the self-directed learning using a bottom-up approach. By looking at self-reported learning logs and interview transcripts an in-depth analysis of the self-directed learning is achieved. The data analysis used constructed grounded theory, which aligns with the bottom-up approach where the learner data is coded and investigated in an open, yet evidence-based way, leaving room for insights to emerge from the learner data. The data corpus is based on 56 participants following three FutureLearn MOOCs, providing 147 learning logs and 19 semi-structured one-on-one interviews with a selection of participants. The results show five specific areas in which learners react with either the material or other learners to self-direct their learning: context, individual or social learning, technology and media provided in the MOOCs, learner characteristics and organising learning. This study also indicates how intrinsic motivation and personal learning goals are the main inhibitors or enablers of self-directed learning

Towards Truly Accessible MOOCs for Persons with Cognitive Disabilities: Design and Field Assessment

International audienceMOOCs are playing an increasingly important role in education systems. Unfortunately, MOOCs are not fully accessible. In this paper, we propose design principles to enhance the accessibility of MOOC players, especially for persons with cognitive disabilities. These principles result from a participatory design process gathering 7 persons with disabilities and 13 expert professionals. They are also inspired by various design approaches (Universal Design for Learning, Instructional Design, Environmental Support). We also detail the creation of a MOOC player offering a set of accessibility features that users can alter according to their needs and capabilities. We used it to teach a MOOC on digital accessibility. Finally, we conducted a field study to assess learning and usability outcomes for persons with cognitive and non-cognitive impairments. Results support the effectiveness of our player for increasing accessibility

Retrieval Practice and Study Planning in MOOCs: Exploring Classroom-Based Self-regulated Learning Strategies at Scale

Massive Open Online Courses (MOOCs) are successful in delivering educational resources to the masses, however, the current retention rates—well below 10 %—indicate that they fall short in helping their audience become effective MOOC learners. In this paper, we report two MOOC studies we conducted in order to test the effectiveness of pedagogical strategies found to be beneficial in the traditional classroom setting: retrieval practice (i.e. strengthening course knowledge through actively recalling information) and study planning (elaborating on weekly study plans). In contrast to the classroom-based results, we do not confirm our hypothesis, that small changes to the standard MOOC design can teach MOOC learners valuable self-regulated learning strategies.Teaching and Teacher Learning (ICLON

The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data. Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysi

Student engagement and perceptions of blended-learning of a clinical module in a veterinary degree program.

Blended learning has received much interest in higher education as a way to increase learning efficiency and effectiveness. By combining face-to-face teaching with technology-enhanced learning through online resources, students can manage their own learning. Blended methods are of particular interest in professional degree programs such as veterinary medicine in which students need the flexibility to undertake intra- and extramural activities to develop the range of competencies required to achieve professional qualification. Yet how veterinary students engage with blended learning activities and whether they perceive the approach as beneficial is unclear. We evaluated blended learning through review of student feedback on a 4-week clinical module in a veterinary degree program. The module combined face-to-face sessions with online resources. Feedback was collected by means of a structured online questionnaire at the end of the module and log data collected as part of a routine teaching audit. The features of blended learning that support and detract from students’ learning experience were explored using quantitative and qualitative methods. Students perceived a benefit from aspects of face-to-face teaching and technology-enhanced learning resources. Face-to-face teaching was appreciated for practical activities, whereas online resources were considered effective for facilitating module organization and allowing flexible access to learning materials. The blended approach was particularly appreciated for clinical skills in which students valued a combination of visual resources and practical activities. Although we identified several limitations with online resources that need to be addressed when constructing blended courses, blended learning shows potential to enhance student-led learning in clinical courses

Scaling up behavioral science interventions in online education

Online education is rapidly expanding in response to rising demand for higher and continuing education, but many online students struggle to achieve their educational goals. Several behavioral science interventions have shown promise in raising student persistence and completion rates in a handful of courses, but evidence of their effectiveness across diverse educational contexts is limited. In this study, we test a set of established interventions over 2.5 y, with one-quarter million students, from nearly every country, across 247 online courses offered by Harvard, the Massachusetts Institute of Technology, and Stanford. We hypothesized that the interventions would produce medium-to-large effects as in prior studies, but this is not supported by our results. Instead, using an iterative scientific process of cyclically preregistering new hypotheses in between waves of data collection, we identified individual, contextual, and temporal conditions under which the interventions benefit students. Self-regulation interventions raised student engagement in the first few weeks but not final completion rates. Value-relevance interventions raised completion rates in developing countries to close the global achievement gap, but only in courses with a global gap. We found minimal evidence that state-of-the-art machine learning methods can forecast the occurrence of a global gap or learn effective individualized intervention policies. Scaling behavioral science interventions across various online learning contexts can reduce their average effectiveness by an order-of-magnitude. However, iterative scientific investigations can uncover what works where for whom