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

Recommender systems research and theory in higher education: A systematic literature review

Recommender systems provide the ability to personalize and adapt environments for student learning. To customize learning experiences, applications of recommender systems research in education have resulted in evidence of various recommender system approaches such as content-based filtering, collaborative filtering, and knowledge-based. This research focuses on those applications in higher education when given a non-MOOC classroom setting and examines the theoretical basis for the approaches. Learning and information systems theories are considered in this systematic review of the literature published from 2017 to early 2022. Findings indicate varying adaptive learning design recommender approaches and the potential to build the theoretical base of both learning and information systems theories

A Self-Regulated Learning Approach to Educational Recommender Design

The field of education has the potential to better facilitate student learning by employing educational recommenders that adapt the learning process to the needs of individual learners. 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 of these systems. The theory considered here, self-regulated learning, focuses on putting students in control of their learning and is appropriate in situations where learning is autonomous. This research proposes a design science approach to investigate a theoretical base of self-regulated learning (SRL) for a knowledge-based recommender design framework. Existing research on knowledge-based recommender design with an inclusion of an ontology component will guide development of this artifact. Anticipated results include the formation of design principles to inform the creation of SRL guided recommenders for both practical applications and future research

Leveraging academic initiatives to advance implementation practice: a scoping review of capacity building interventions

Abstract Background Academic institutions building capacity for implementation scholarship are also well positioned to build capacity in real world health and human service settings. How practitioners and policy makers are included and trained in implementation capacity-building initiatives, and their impact on building implementation practice capacity is unclear. This scoping review identified and examined features of interventions that build implementation practice capacity across researchers and practitioners or practitioners-in-training. Methods Five bibliographic databases were searched. Eligible studies (a) described an implementation capacity building intervention with a connection to an academic institution, (b) targeted researchers and practitioners (including practitioners-in-training, students, or educators), and (c) reported intervention or participant outcomes. Articles that only described capacity building interventions without reporting outcomes were excluded. Consistent with Arksey and O’Malley’s framework, key study characteristics were extracted (target participants, core components, and outcomes) and analyzed using open coding and numerical analysis. Results Of 1349 studies identified, 64 met eligibility for full-text review, and 14 were included in the final analysis. Half of the studies described implementation capacity building interventions that targeted health or behavioral health researchers, practitioners, and practitioners-in-training together, and half targeted practitioners or practitioners-in-training only. The most common components included structured didactic activities offered in person or online, mentorship and expert consultation to support implementation, and practical application activities (e.g., field placements, case studies). Knowledge sharing activities and technical assistance were less common. All studies reported favorable outcomes related to knowledge attainment, increased ability to implement evidence, productivity, and satisfaction. Conclusions Building implementation capacity among practitioners is critical for integrating insights from implementation science into the field and preventing the “secondary” implementation research-to-practice gap. This scoping review identified several promising implementation practice capacity building interventions that tend to build practitioner capacity via expert led activities which may be relevant for academic institutions seeking to build implementation practice capacity. To avoid widening the implementation research-to-practice gap, implementation capacity building interventions are needed that target policy makers, expand beyond multiple practice settings, and leverage university/community partnerships or on-site academic medical centers. Future studies will also be needed to test the impact on service quality and public health outcomes