Assessing Self-Regulation of Learning Dimensions in a Stand-alone MOOC Platform

A capacity for self-regulated learning (SRL) has long been recognised as an important factor in successful studies. Although educational researchers have started to investigate the concept of SRL in the context of online education, very little is yet known about SRL in relation to massive open online courses (MOOCs) or of appropriate strategies to foster SRL skills in MOOC learners. Self-regulation is particularly important in a MOOC-based study, which demands effective independent learning, and where widely acknowledged high dropout rates are observed. This study reports an investigation and assessment of the concept of SRL using a novel MOOC platform (eLDa) by providing study options (either via a self-directed learning or instructor-led learning) using a novel learning tool. In view of this, the research presents general description of self-regulated learning and explored the various existing dimensions used to expose the learners SRL skills. Drawing comparison of the online tool, the results and findings of the data were analysed. The study dis¬cusses how the various dimensions contributed to the knowledge representation of the self-regulated learning abilities shown by the learners. We present how these SRL dimensions captured using the measuring instrument contributes to our growing understanding of the distinctive features of the individual learner’s self-regulated learning. MOOCs success required a high performance of self-regulated learning abilities which at the moment very little has shown these degree of supporting SRL skills. This paper presents preliminary evaluation of a novel e-learning tool known, as ‘eLDa’ developed to implement this investi¬gation of self-regulation of learning. The research applied a modified online self-regulated learning questionnaire (OSLQ) as the instrument to measure the SRL skills. The modified questionnaire known as MOOC OSLQ (MOSLQ) was developed with a 19-item scale questions that exposes the six SRL dimensions used in this study

A Metacognitive Instructional Approach and Self-Reflection: Reflective Practice From a Computer Science Perspective

Teaching is increasingly complex work which takes time to plan and continuous effort to ensure the highest standards of professional practice. There is a paradox in our rapidly changing society that educators are not very open to change in their practice (Hoban, 2002). The process for educators to learn and acquire effective teaching skills is a labour which has to be mastered over time. They would gain pedagogical knowledge and skills based on accumulation of new acquired knowledge and teaching methods and strategies to be added to their repertoire of existing knowledge and skills. In addition, it is vital for educators to utilize two aspects of reflective practice as suggested by Schon, which are ‘reflection in action’ and ‘reflection on action’ (Schon, 1983, 1987). Reflection in action refers to quick thinking action which takes places when one is teaching in the classroom. On the other hand, reflection on action usually takes place after the lesson out of the classroom when the educator reflects on his or her previous teaching and considers certain situations from the lesson again. Educational system in the 21st century encompasses reflection from both the teachers and students’ perspectives. In general, reflective practice in teaching and learning in undergraduate education focuses on the professional development of students and academics in an interdisciplinary education. Reflective practice has been in existence in most professional educational practices for several centuries. The use of reflective practice in computing education courses has significant benefits to enhance the knowledge of the students. However, there is some controversies on how this reflection was done and the manner in which this was done based on individual teaching practice. One of the main objectives of this study is to describe the various approaches used in teaching undergraduate students in a computing course. The study illustrates several modern approaches used during this classes. A qualitative research method was applied in gathering the feedback from the students using a general survey questions based on the course delivery. The study used statistical packages for the social sciences (SPSS) to analyze the data gathered. The results revealed the various level of acceptance of the teaching methods applied in the course. These results also demonstrate significant findings on the students' opinions and criticism which could help in future improvement of undergraduate computing curriculum. The study, firstly review literature on reflective practice. Secondly, discussed some of the good teaching practices and methods used in delivery the classes. Thirdly, the analysis and results obtained from the instrument questionnaires used for this study and finally, summary of the findings and further research directions

Investigating self-regulation in the context of a blended learning computing course

PURPOSE: Massive open online courses (MOOCs) provide an innovative educational technology, which has become widely used for distance learning by independent learners. However, there has been little work so far to study the effects of using MOOCs as part of a blended classroom approach in which learning activities take place both online and in a traditional classroom setting. The purpose of this study is to investigate the aspects of blended MOOC usage in the context of a computing course for first-year undergraduates at a UK university. DESIGN/METHODOLOGY/APPROACH: The MOOC was implemented on a purpose-built platform that supports learners to make informed choices about their learning path. This research investigates students’ capacity for self-regulated learning (SRL) and understands their preparedness for independent study, profile the general areas of SRL strength and weakness, which may affect their ability to learn effectively in a self-directed environment. An existing survey instrument, based on a six-dimensional conceptualization of SRL was adapted to investigate the self-regulation in the MOOC study. FINDINGS: The results demonstrate that the dimensions of self-evaluation and time management represent particular areas of weakness for these students. Furthermore, profiles of SRL for individual students show considerable differences in capability within the study. However, the deficiencies in SRL dimensions contrast with the students’ of generally high levels of attainment. This leads us to question the validity of the existing SRL. Furthermore, a high level of social interaction and help-seeking was reported in relation to the MOOC study indicating the increasing importance of social learning and the importance of co-regulation for SRL. RESEARCH LIMITATIONS/IMPLICATIONS: Although this study presents findings from a small data sample, it points to a number of areas for future implementation and exploration. Firstly, in line with the action research approach, students’ SRL could, in the future, be tested early in the course with the MOOC component being ideally placed to provide personalised support for each student in aspects which they may benefit from developing further. Secondly, for students in the cohort studied in this paper, a longitudinal study will track how their SRL develops as they progress through the degree. We feel that it is important to gain further qualitative data to understand how students work in practice and the strategies they adopt when confronted with different modes of learning. Finally, it is necessary to consider the conceptualisation of SRL to understand if existing instruments could be adapted to provide a more accurate assessment of the effectiveness of learners’ self-regulation. ORIGINALITY/VALUE: There has been little research on the effects of using a MOOC as the online component of a blended classroom learning approach. This study has used a theoretical perspective of SRL to investigate the approaches to self-regulation adopted by undergraduate computer science students studying in a blended MOOC environment. The MOOC used for this purpose was developed on the innovative eLDa platform, allowing students to determine, track and visualise their individual path through topics and materials offered in the MOOC

An investigation of self‑regulated learning in a novel MOOC platform

Copyright © The Author(s) 2022. Despite the proliferation of massive open online courses (MOOCs) and the impressive levels of enrolment they attract, many participants do not complete these courses. High drop-out has been identified as one of the major problems with existing MOOC formats. Our work addresses two factors relating to non-completion. Firstly, MOOCs require a high degree of self-regulated learning (SRL) skills but most do not adequately develop such skills, thus making them inaccessible in practice to many. Related to this is the inflexibility and passivity of many current MOOC formats, preventing individuals from setting their own learning objectives and directing their own learning. This paper presents preliminary findings from an investigation into MOOC learners’ SRL skills and the relationship to how participants learn. Following a design science methodology, we have developed a novel MOOC platform to support learner choice and to assist participants in defining learning goals and developing individual study paths. This paper describes the architecture of the system and presents findings from a pilot MOOC developed on the platform. Our results indicate that there is a high demand for more flexible, self-directed learning but that MOOC learners exhibit deficiencies in specific SRL skills including help seeking and task strategies. The contextualised nature of SRL skills means that even learners with a strong background of formal education may not deploy the best strategies for MOOC learning. This work is of significance to MOOC development in general as it highlights the need for targeted strategies to encourage SRL in MOOC platforms and innovation

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