Improving E-Learning by Integrating a Metacognitive Agent

The major disadvantage of the current Learning Management Systems is the lack of learner assistance in their learning processes and, therefore, they can not replace the presence of the teacher who ensures the progress of learning. In fact, we proposed to integrate, for each learner, a metacognitive agent that supported the metacognitive assistance and extracts the defectsin the learning process and strategies. The goal is to invite the learner to correct himself and improve his learning method. Metacognitive questionnaires were distributed to a group of 100 students before, during and after a computer course. The goal is to evaluatethe metacognitive attributes and to determine their influence on the success of learning. Decision trees were used as data analysis tools to extract a set of rules and to discover the influence of these metacognitive attributes on the result obtained by the learners. The results indicate that there are relationships between the different metacognitive attributes and the learners’ success. We note there is the influence of metacognitive incitement on learner outcomes, which reflects the degree of understanding of a learning pedagogical unit by the learner

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

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

Design of a multi-agent system using the "MaSE" method for learners' metacognitive help

This article addresses a multi-agent approach to solving the problem of integrating metacognitive incentives into Learning Management System. The behavior of the teacher in a classroom-teaching situation, where teacher adopting the competency-based approach, is characterized by a set of didactic interventions dynamically adaptable according to the actions-reactions of the learners. These interventions are continually subjected to perfection by experience. In this article, we are interested in modeling the multi-agent system in order to help the learners develop their metacognitive skills in a continuous way. The purpose of this system is to supervise the activities and statements of the learner and communicate them to the metacognitive agent. The latter focuses on the assessment of the learner's metacognitive skills in order to trigger, automatically, metacognitive incentives to provide help messages. Integrating the agent for metacognitive control and assistance, allows learners to maintain motivation and confidence, and elicit their attention to the importance of metacognitive skills during learning activity. The "MaSE" methodology and the "agentTool" are used to model the multi-agent system

Student profiling in a dispositional learning analytics application using formative assessment

How learning disposition data can help us translating learning feedback from a learning analytics application into actionable learning interventions, is the main focus of this empirical study. It extends previous work where the focus was on deriving timely prediction models in a data rich context, encompassing trace data from learning management systems, formative assessment data, e-tutorial trace data as well as learning dispositions. In this same educational context, the current study investigates how the application of cluster analysis based on e-tutorial trace data allows student profiling into different at-risk groups, and how these at-risk groups can be characterized with the help of learning disposition data. It is our conjecture that establishing a chain of antecedent-consequence relationships starting from learning disposition, through student activity in e-tutorials and formative assessment performance, to course performance, adds a crucial dimension to current learning analytics studies: that of profiling students with descriptors that easily lend themselves to the design of educational interventions

Supporting students’ confidence judgement through visualising alignment in open learner models

Supporting students’ knowledge monitoring skills, a component of metacognition, can help students regulate their own learning. This thesis investigates the alignment of learners’ confidence in their knowledge with a computer’s assessment of their knowledge, visualised using an Open Learner Model (OLM). The research explored students’ preferred method for visualising inconsistent data (e.g. misalignment) in an OLM, and the ways in which visualising alignment can influence student interaction with the computer. The thesis demonstrates that visualising alignment in Open Learner Models signifi-cantly increases students’ confidence compared to a control condition. In particular, visualising alignment benefited low-achieving students, in terms of knowledge monitoring and this was associated with improvements in their performance. Students showed a preference towards the visualisations that provides an overview of the in-formation (i.e. opacity) rather than ones, which provide detailed information. Graph-ical representation is shown to be more beneficial in motivating students to interact with the system than text-based representation of the same information in the con-text of representing the alignment within OLMs

The Development and Validation of the Emporium Model Motivation Scale (EMMS)

The purpose of this research study was to begin the development and validation of a new survey instrument; the Emporium Model Motivation Scale (EMMS). The instrument is designed to be used as part of a more holistic evaluation of non-traditional student-centered mathematics courses or programs redesigned using the Emporium Model (E-Model). EdResearch suggested that the design of the E-Model environment was better suited to help students become more autonomy-natured (Williams, 2016). The present research was rooted in Self-determination Theory (SDT), which asserted that all individuals had a natural desire to strive for autonomy, competence, and relatedness in their social environments (Ryan & Deci, 2000; 2017). The research study consisted of a random sample of n = 463 respondents from both a U.S. community college and 4-year public university. Results of an Exploratory Factor Analysis (EFA) produced four parsimonious factor solutions that showed potential to be valid, highly reliable with (ω ≥ .89) and replicable across other samples or populations. The factors were analyzed using Polychoric correlations, with Unweighted Least Squares (ULS) extraction and Promax rotation. Correlational analysis, MANOVA, ANOVA, and Standard Multiple Regression were performed with accurate and reliable standardized factor score estimates. Overall results revealed statistically significant differences between the two institutions of higher learning across levels of the EMMS factors. Further analyses revealed that age was a statistically significant predictor of the EMMS factors and that older respondents were more autonomous and receptive of the E-Model design for course instruction

The impact of digital technologies on teaching and learning

This thesis explores the impact of new technologies on learning and teaching and draws on research work carried out over a ten-year period. The thesis looks at the facilitators and barriers to using digital technologies effectively and explores the challenges for educators as they respond to the changes brought about by these digital technologies. It presents eight published works that have investigated the impact of digital technologies and collected data using a range of qualitative and quantitative techniques. The core paper provides a model by which the impact of digital technologies can be analysed and understood and the remaining papers populate that model. The model identifies a system of learning spaces that describe the ways that learners, teachers and managers respond to the challenges and opportunities that digital technologies bring to learning. The papers explore how the school space, the teaching space, the personal learning space and the living space have all been transformed by digital technologies. These papers highlight the ongoing tension within education brought about by the conflicting ambitions of managers to control learning while at the same time encouraging personalisation. The papers consider the nature of digital divides and also the potential hazards presented to young people by digital technologies. Finally, the papers explore the relationship between the use of digital technologies and academic achievement. The work presented here provides a coherent contribution to the field that offers new understandings of the impact of digital technologies on learning, and identifies key issues for further research