Detecting Self-Regulated Learning in Online Communities by Means of Interaction Analysis

Abstract--Interaction analysis is increasingly used to study learning dynamics within online communities. This paper aims to investigate whether Interaction Analysis can help understand the practice and development of Self-Regulated Learning (SRL) in Virtual Learning Communities (VLCs). To this end, a set of SRL indicators is proposed to spot clues of self-regulated events within students' messages. Such clues have been identified and classified according to Zimmerman's SRL model and some subsequent studies concerning SRL in Technology Enhanced Learning Environments (TELEs). They have been tested on the online component of a blended course for trainee teachers, by analyzing the messages exchanged by a group of learners in two modules of the course. The results of this analysis have been compared with those of a previous study carried out, with more traditional methods, on the same course. The similarity of the results obtained by the two approaches suggests that Interaction Analysis is an effective, though rather labor-intensive, methodology to study SRL in online learning communities

Scaffolding Reflection: Prompting Social Constructive Metacognitive Activity in Non-Formal Learning

The study explores the effects of three different types of non-adaptive, metacognitive scaffolding on social, constructive metacognitive activity and reflection in groups of non-formal learners. Six triads of non-formal learners were assigned randomly to one of the three scaffolding conditions: structuring, problematising or epistemological. The triads were then asked to collaboratively resolve an ill-structured problem and record their deliberations. Evidence from think-aloud protocols was analysed using conversational and discourse analysis. Findings indicate that epistemological scaffolds produced more social, constructive metacognitive activity than either of the two other scaffolding conditions in all metacognitive activities except for task orientation, as well as higher quality interactions during evaluation and reflection phases. However, participants appeared to be less aware of their activities as forming a strategic, self-regulatory response to the problem. This may indicate that for learning transfer, it may be necessary to employ an adaptive, facilitated reflection on learners' activities

Community tracking in a cMOOC and nomadic learner behavior identification on a connectivist rhizomatic learning network

This article contributes to the literature on connectivism, connectivist MOOCs (cMOOCs) and rhizomatic learning by examining participant interactions, community formation and nomadic learner behavior in a particular cMOOC, #rhizo15, facilitated for 6 weeks by Dave Cormier. It further focuses on what we can learn by observing Twitter interactions particularly. As an explanatory mixed research design, Social Network Analysis and content analysis were employed for the purposes of the research. SNA is used at the macro, meso and micro levels, and content analysis of one week of the MOOC was conducted using the Community of Inquiry framework. The macro level analysis demonstrates that communities in a rhizomatic connectivist networks have chaotic relationships with other communities in different dimensions (clarified by use of hashtags of concurrent, past and future events). A key finding at the meso level was that as #rhizo15 progressed and number of active participants decreased, interaction increased in overall network. The micro level analysis further reveals that, though completely online, the nature of open online ecosystems are very convenient to facilitate the formation of community. The content analysis of week 3 tweets demonstrated that cognitive presence was the most frequently observed, while teaching presence (teaching behaviors of both facilitator and participants) was the lowest. This research recognizes the limitations of looking only at Twitter when #rhizo15 conversations occurred over multiple platforms frequented by overlapping but not identical groups of people. However, it provides a valuable partial perspective at the macro meso and micro levels that contribute to our understanding of community-building in cMOOCs

Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation

SimProgramming : the development of an integrated teaching approach for computer programming in higher education

Conferência realizada em Valência de 7-9 de março de 2016Computer programming courses in higher education tend to have high rates of academic failure and students struggle, particularly so in the transition from entry-level programming to advanced programming. Some of the reasons given in the literature relate to the type of teaching approach and the strategies used by students and their attitudes towards computer programming. The literature also mentions that educational approaches are not always appropriate to the needs of students and to the development of skills required in the job market. We developed a teaching approach to try to address some of these issues and support students learning computer programming in the transition from entry-level to advanced computer programming: the SimProgramming approach. This approach was introduced at the University of Trás-os-Montes e Alto Douro (Portugal), within the scope of the course “Programming Methodologies III”, part of the second curricular year of the programmes of studies in Informatics Engineering and in Information & Communication Technologies. We present in detail the origins of the SimProgramming approach, starting from the first trials that introduced, in two iterations, learning activities based on problem-based learning, and up to the third iteration where the current SimProgramming approach was implemented. We describe the reasoning, design and implementation of these three iterations, to show how the approach evolved. The SimProgramming approach is based in four conceptual foundations: business-like learning environment, self-regulated learning, co-regulated learning and formative assessment. For each of these conceptual foundations, we explain the teaching strategies adopted. In SimProgramming, the learning activity process develops in four phases, and students have specific tasks in each phase. We analyse interview data regarding student perceptions about the SimProgramming approach, and registration grids data on team work dynamics and final assessment of the assignment, noting the impact of SimProgramming in student grades. The application of SimProgramming revealed promising evidences in the overall results of student learning in the activities proposed in this approach. The average grades improved, and did the number of students regularly submitting their tasks on schedule. The perceptions of students regarding the SimProgramming approach are very positive: they recommend using it in the following years, and provided some suggestions to improve the approach. We conclude with reflections and recommendations for subsequent development of the SimProgramming approach in its application to the teaching of computer programming and potential for using it in other educational contexts.FC

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks