2,296 research outputs found
RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Various forms of Peer-Learning Environments are increasingly being used in
post-secondary education, often to help build repositories of student generated
learning objects. However, large classes can result in an extensive repository,
which can make it more challenging for students to search for suitable objects
that both reflect their interests and address their knowledge gaps. Recommender
Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution
to this problem by providing sophisticated filtering techniques to help
students to find the resources that they need in a timely manner. Here, a new
RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is
presented. The approach uses a collaborative filtering algorithm based upon
matrix factorization to create personalized recommendations for individual
students that address their interests and their current knowledge gaps. The
approach is validated using both synthetic and real data sets. The results are
promising, indicating RiPLE is able to provide sensible personalized
recommendations for both regular and cold-start users under reasonable
assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the
Journal of Educational Data Minin
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
In this paper, we suggest a novel method to aid lifelong learners to access
relevant OER based learning content to master skills demanded on the labour
market. Our software prototype 1) applies Text Classification and Text Mining
methods on vacancy announcements to decompose jobs into meaningful skills
components, which lifelong learners should target; and 2) creates a hybrid OER
Recommender System to suggest personalized learning content for learners to
progress towards their skill targets. For the first evaluation of this
prototype we focused on two job areas: Data Scientist, and Mechanical Engineer.
We applied our skill extractor approach and provided OER recommendations for
learners targeting these jobs. We conducted in-depth, semi-structured
interviews with 12 subject matter experts to learn how our prototype performs
in terms of its objectives, logic, and contribution to learning. More than 150
recommendations were generated, and 76.9% of these recommendations were treated
as useful by the interviewees. Interviews revealed that a personalized OER
recommender system, based on skills demanded by labour market, has the
potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of
CSEDU 2020 by SciTePres
Lessons Learned from Development of a Software Tool to Support Academic Advising
We detail some lessons learned while designing and testing a
decision-theoretic advising support tool for undergraduates at a large state
university. Between 2009 and 2011 we conducted two surveys of over 500 students
in multiple majors and colleges. These surveys asked students detailed
questions about their preferences concerning course selection, advising, and
career paths. We present data from this study which may be helpful for faculty
and staff who advise undergraduate students. We find that advising support
software tools can augment the student-advisor relationship, particularly in
terms of course planning, but cannot and should not replace in-person advising.Comment: 5 Figures, revised version including more figures and
cross-referencin
Parameterized Exercises in Java Programming: using Knowledge Structure for Performance Prediction
In this paper, we study the effect of using domain knowledge structure on predicting student performance with parameterized Java programming exercises. Domain knowledge structure defines connections between elementary knowledge items. While known to be beneficial in general, it has not been used to predict performance.We compare five different approaches for this purpose: Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), and three dimensional Bayesian Probabilistic Tensor Factorization (3D-BPTF), that are not able to take into account knowledge structure; and four-dimensional Bayesian Probabilistic Tensor Factorization (4D-BPTF) and Feature-Aware Student Knowledge Tracing (FAST), that can take into account knowledge structure. We approach the problem using both topic-level and question-level Knowledge Components (KCs) and test the methods on a dataset of parameterized questions. Our work is the first in the field that models students’ behavior in a four dimensional tensor. Our experiments show that, when having only the knowledge-item-level information, all of the models work similarly in predicting student performance, but adding the topic-level information that integrates knowledge items changes the performance of these models in different directions
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
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