Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

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

Self-regulated learning in higher education : identifying key component processes

The concept of self-regulated learning is becoming increasingly relevant in the study of learning and academic achievement, especially in higher education, where quite distinctive demands are placed on students. Though several key theoretical perspectives have been advanced for self-regulated learning, there is consensus regarding the central role played by student perceptions of themselves as learners. There are two general aims of this positional article. The first is to emphasise self-regulated learning as a relevant and valuable concept in higher education. The second is to promote the study of those constituent elements considered most likely to develop our understanding beyond a mere description of those processes thought to be involved in self-regulated learning. A case is presented for learning style, academic control beliefs and student self-evaluation as key constructs which contribute to an increased understanding of student self-regulated learning and which facilitate the application of self-regulated learning in pedagogy by enhancing its tangibility and utility

Maximizing Competency Education and Blended Learning: Insights from Experts

In May 2014, CompetencyWorks brought together twenty-three technical assistance providers to examine their catalytic role in implementing next generation learning models, share each other's knowledge and expertise about blended learning and competency education, and discuss next steps to move the field forward with a focus on equity and quality. Our strategy maintains that by building the knowledge and networks of technical assistance providers, these groups can play an even more catalytic role in advancing the field. The objective of the convening was to help educate and level set the understanding of competency education and its design elements, as well as to build knowledge about using blended learning modalities within competency-based environments. This paper attempts to draw together the wide-ranging conversations from the convening to provide background knowledge for educators to understand what it will take to transform from traditional to personalized, competency-based systems that take full advantage of blended learning

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

The use of animated agents in e‐learning environments: an exploratory, interpretive case study

There is increasing interest in the use of animated agents in e‐learning environments. However, empirical investigations of their use in online education are limited. Our aim is to provide an empirically based framework for the development and evaluation of animated agents in e‐learning environments. Findings suggest a number of challenges, including the multiple dialogue models that animated agents will need to accommodate, the diverse range of roles that pedagogical animated agents can usefully support, the dichotomous relationship that emerges between these roles and that of the lecturer, and student perception of the degree of autonomy that can be afforded to animated agents

Systematic Review of Adaptive Learning Research Designs, Context, Strategies, and Technologies From 2009 to 2018

This systematic review of research on adaptive learning used a strategic search process to synthesize research on adaptive learning based on publication trends, instructional context, research methodology components, research focus, adaptive strategies, and technologies. A total of 61 articles on adaptive learning were analyzed to describe the current state of research and identify gaps in the literature. Descriptive characteristics were recorded, including publication patterns, instructional context, and research methodology components. The count of adaptive learning articles published fluctuated across the decade and peaked in 2015. During this time, the largest concentration of adaptive learning articles appeared in Computers and Education. The majority of the studies occurred in higher education in Taiwan and the United States, with the highest concentration in the computer science discipline. The research focus, adaptive strategies, and adaptive technologies used in these studies were also reviewed. The research was aligned with various instructional design phases, with more studies examining design and development, and implementation and evaluation. For examining adaptive strategies, the authors examined both adaptive sources based on learner model and adaptive targets based on content and instructional model. Learning style was the most observed learner characteristic, while adaptive feedback and adaptive navigation were the most investigated adaptive targets. This study has implications for adaptive learning designers and future researchers regarding the gaps in adaptive learning research. Future studies might focus on the increasing availability and capacities of adaptive learning as a learning technology to assist individual learning and personalized growth