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

Professional self-improvement of social workers in working with female combatants: the potential of the МООС

Processes of the digital transformation of all spheres of life in modern Ukrainian society and the introduction of emergency distance learning in wartime conditions in Ukraine have caused the emergence of new challenges in the professional self-improvement of social workers in order to provide quality social services to a new target group of clients - participants in combat operations in conditions of uncertainties and threats of wartime. The authors of the article investigated the ICT used by social workers of Kyiv (Ukraine) working with combatants in state and non-governmental organizations. The study has indicated a satisfactory level of ICT use in professional activities with this target group and the need to increase the professional competence of workers providing services to this target group. The problems of combatants addressed to social workers were explored. Theoretical analysis of the content and structure of online open courses that can be used for professional self-improvement of social workers related to improving their digital and methodological competence in working with combatants was carried out. A wide range of theoretical research methods (analysis, synthesis, comparison, generalization, etc.) was used to achieve the goal. Based on the analysis of MOOC platforms available on the domestic and international Internet market of educational services, it was established that Coursera, EdEra, and Prometheus are the most popular among social workers. It was found that all courses on MOOC platforms have a similar structure, which includes interactive video lectures, educational resources, online tests of various types, and a forum for discussing issues and organizing a discussion. The main MOOCs that can be used by social workers in working with female combatants are presented

Ontology-Enhanced Educational Annotation Activities

Information and communications technology and technology-enhanced learning have unquestionably transformed traditional teaching–learning processes and are positioned as key factors to promote quality education, one of the basic sustainable development goals of the 2030 agenda. Document annotation, which was traditionally carried out with pencil and paper and currently benefits from digital document annotation tools, is a representative example of this transformation. Using document annotation tools, students can enrich the documents with annotations that highlight the most relevant aspects of these documents. As the conceptual complexity of the learning domain increases, the annotation of the documents may require comprehensive domain knowledge and an expert analysis capability that students usually lack. Consequently, a proliferation of irrelevant, incorrect, and/or poorly decontextualized annotations may appear, while other relevant aspects are completely ignored by the students. The main hypothesis proposed by this paper is that the use of a guiding annotation ontology in the annotation activities is a keystone aspect to alleviate these shortcomings. Consequently, comprehension is improved, exhaustive content analysis is promoted, and meta-reflective thinking is developed. To test this hypothesis, we describe our own annotation tool, @note, which fully implements this ontology-enhanced annotation paradigm, and we provide experimental evidence about how @note can improve academic performance via a pilot study concerning critical literary annotation

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

Virtual Reality in Mathematics Education (VRiME):An exploration of the integration and design of virtual reality for mathematics education

This thesis explores the use of Virtual Reality (VR) in mathematics education. Four VR prototypes were designed and developed during the PhD project to teach equations, geometry, and vectors and facilitate collaboration.Paper A investigates asymmetric VR for classroom integration and collaborative learning and presents a new taxonomy of asymmetric interfaces. Paper B proposes how VR could assist students with Autism Spectrum Disorder (ASD) in learning daily living skills involving basic mathematical concepts. Paper C investigates how VR could enhance social inclusion and mathematics learning for neurodiverse students. Paper D presents a VR prototype for teaching algebra and equation-solving strategies, noting positive student responses and the potential for knowledge transfer. Paper E investigates gesture-based interaction with dynamic geometry in VR for geometry education and presents a new taxonomy of learning environments. Finally, paper F explores the use of VR to visualise and contextualise mathematical concepts to teach software engineering students.The thesis concludes that VR offers promising avenues for transforming mathematics education. It aims to broaden our understanding of VR's educational potential, paving the way for more immersive learning experiences in mathematics education

Self-Regulated Learning for Chinese, Adult Language Learners: An Intervention Study in a Blended Learning Environment

Blended learning is a well-established learning design providing much needed accessibility to learning resources and improved pedagogy through technological means. The flipped classroom model is one approach that can help promote engagement through the prioritization of learner-led discussions and collaborative work in the classroom while extending access to language learning practice outside of class time (Bergman & Sams, 2012; Forsey et al., 2013; Johnson & Marsh, 2016). Implicit within the design, however, is the introduction of non-linear access to information which often requires learners to assume more responsibility for their learning process, deploying self-regulated learning strategies to achieve their objectives (Perez-Alvarez et al., 2018). My dissertation explores the increased need for self-regulated learning experienced by Chinese, adult English language learners for achieving success in a blended, flipped learning environment. As a design-based research study, my focus was on the overarching objective of the development of an intervention. This objective was addressed in three, iterative stages of research involving the analysis of the context, and the design, development, and subsequent evaluation of prototypes. This process led to the creation of some initial design principles that were used to guide the development of a digital app that was deployed to a small group of participants. During the implementation and evaluation of the app-based intervention, an additional research objective relating to achievement goal orientation was adopted to explore the types of goals that language learners with high persistence were likely to pursue. Multiple, qualitative data sources were used to address the research questions including document analysis, focus groups, interviews, and field observations. Findings that emerged from the study contributed to the refinement of design principles and provided insight for subsequent development of the intervention. Findings suggested that personalized instructor feedback fulfilled an important emotional function for learners in this context. Enabling a dialogical feedback process between participants and the instructor helped engage learners in more thoughtful self-assessment using external feedback including data visualizations. This process contributed to the development of trust in the source of the feedback, which was more likely to lead to a change in behaviour. Additional insights concerning achievement goals were derived from the interviews, suggesting benefits of multiple different achievement goal profiles could be found. These findings lend further support to the value of using qualitative methods for investigating learner goal-orientations. This study included a small group of learners who demonstrated high persistence. It was recommended that future research involve a larger sample of learners to explore variations in response to the intervention to improve the effectiveness of the design and implementation

Tracing learning strategies in online learning environments: a learning analytics approach

Learning has expanded beyond formal education; yet, students continue to face the challenge of how to effectively direct their learning. Among the processes of learning, the selection and application of learning tactics and strategies are fundamental steps. Learning tactics and strategies have long been considered as key predictors of learning performance. Theoretical models of self-regulated learning (SRL) assert that the choice and use of learning tactics and strategies are influenced by the internal (cognitive) and external (task) conditions. These conditions are consistently updated when students receive internal/external feedback. However, internal feedback generated based on students’ evaluation of their own performance against the expectation and/or learning goal is not accurate. Guiding students to apply appropriate learning strategies i.e. providing external feedback, hence, could enhance the students’ learning. Recent research literature suggests that learning analytics can be leveraged to support students in the selection and use of effective learning tactics and strategies. However, there has been limited literature on the ways this can be achieved. This thesis aims to fill this gap in the literature. This thesis begins by exploring the state of the art regarding how students receive learning analytics-based support for the selection and application of learning tactics and strategies. The systematic literature review on this topic reveals that students rarely receive feedback on learning tactics and strategies with learning analytics dashboards. One of the barriers to providing feedback on learning tactics and strategies is the difficulty in detecting learning tactics and strategies that students used when interacting with learning activities. Hence, this thesis proposes a novel analyticsbased approach to detect learning tactics and strategies based on digital trace data recorded in learning environments. The proposed analytics-based approach is based on process, sequence mining and clustering techniques. To validate the results of the proposed approach and the credibility of the automatically detected learning tactics and strategies, associations with academic performance and different feedback conditions are explored. To further validate the approach, the efficacy of each proposed approach in the detection of learning tactics and strategies is investigated. In addition, the thesis explores the alignment of the automatically detected learning tactics and strategies with relevant models of SRL. This is done by examining the association between the internal conditions and external conditions. Specifically, internal conditions are represented by the disposition of students based on self-reports of personality traits, whereas external conditions are represented by course instructional designs and delivery modalities. The thesis is concluded with a discussion of the implications of the proposed analytics methodology on research and practice of learning and teaching

5th International Open and Distance Learning Conference Proceedings Book = 5. Uluslararası Açık ve Uzaktan Öğrenme Konferansı Bildiri Kitabı

In celebration of our 40th anniversary in open and distance learning, we are happy and proud to organize the 5th International Open & Distance Learning Conference- IODL 2022, which was held at Anadolu University, Eskişehir, Türkiye on 28-30 September 2022. After the conferences in 2002, 2006, 2010, and 2019, IODL 2022 is the 5th IODL event hosted by Anadolu University Open Education System (OES)