A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

Towards the Situated Engagement Evaluation Model (SEEM) : making the invisible visible

This thesis explores the multifaceted concept of engagement within online learning environments. Key research aims are to suggest approaches and an extendable model for evaluating, monitoring and developing understanding of online learner engagement. The overall intention is to offer educators insight, practical guidance and tools for supporting timely intervention in fostering learner engagement. This thesis reviews the major theoretical perspectives on learning and highlights the role of student engagement in relation to the research literature. It discusses the limitations of the methods applied in current research and attempts to address this problem by crossing the disciplinary boundaries to draw together a range of perspectives and methodologies. A review of the literature provides a foundation for a learner engagement evaluation model that employs a variety of evaluation methods and accommodates the possible diversity of learning experiences. The proposed ‘Situated Engagement Evaluation Model’ (SEEM) is positioned to reflect the wide theoretical perspective of social learning. It constitutes a comprehensive system of intertwined components (Learning Content; Pedagogical Design Elements; Learning Profiles; and Dialogue and Communication) that learners may interact with, and integrates dynamically changing preferences and predispositions (e.g. cultural, emotional, cognitive) potentially informative in engagement studies. Prior to (and independently of) the development of SEEM, four empirical studies were conducted and reported here. These explored patterns of online engagement with respect to learning content, learning profiles, patterns of communication and elements of pedagogical design. Studies were then revisited to evaluate the usefulness of SEEM for monitoring and evaluating student engagement, and to discuss its potential for guiding intervention to improve learning experiences. The practical relevance for integrated and automated implementation of SEEM in online learning is considered further

A Bayesian Network Based on the Differentiated Pedagogy to Generate Learning Object According to FSLSM

In this paper we propose a model that generates learning objects in an adaptive learning system according to the Felder-Silverman learning styles, based on the Bayesian networks and taking into consideration the recommendations of the differentiated pedagogy which requires creating multiple versions of the same learning object. The proposed model includes also correcting the non-learning paths which is the main reason behind the choice of versioning the learning objects

Manager’s and citizen’s perspective of positive and negative risks for small probabilities

So far „risk‟ has been mostly defined as the expected value of a loss, mathematically PL, being P the probability of an adverse event and L the loss incurred as a consequence of the event. The so called risk matrix is based on this definition. Also for favorable events one usually refers to the expected gain PG, being G the gain incurred as a consequence of the positive event. These “measures” are generally violated in practice. The case of insurances (on the side of losses, negative risk) and the case of lotteries (on the side of gains, positive risk) are the most obvious. In these cases a single person is available to pay a higher price than that stated by the mathematical expected value, according to (more or less theoretically justified) measures. The higher the risk, the higher the unfair accepted price. The definition of risk as expected value is justified in a long term “manager‟s” perspective, in which it is conceivable to distribute the effects of an adverse event on a large number of subjects or a large number of recurrences. In other words, this definition is mostly justified on frequentist terms. Moreover, according to this definition, in two extreme situations (high-probability/low-consequence and low-probability/high-consequence), the estimated risk is low. This logic is against the principles of sustainability and continuous improvement, which should impose instead both a continuous search for lower probabilities of adverse events (higher and higher reliability) and a continuous search for lower impact of adverse events (in accordance with the fail-safe principle). In this work a different definition of risk is proposed, which stems from the idea of safeguard: (1Risk)=(1P)(1L). According to this definition, the risk levels can be considered low only when both the probability of the adverse event and the loss are small. Such perspective, in which the calculation of safeguard is privileged to the calculation of risk, would possibly avoid exposing the Society to catastrophic consequences, sometimes due to wrong or oversimplified use of probabilistic models. Therefore, it can be seen as the citizen‟s perspective to the definition of risk