Designing and Evaluating an Educational Recommender System with Different Levels of User Control
Educational recommender systems (ERSs) play a crucial role in personalizing learning experiences and enhancing educational outcomes by providing recommendations of personalized resources and activities to learners, tailored to their individual learning needs. However, their effectiveness is often diminished by insufficient user control and limited transparency. To address these challenges, in this paper, we present the systematic design and evaluation of an interactive ERS, in which we introduce different levels of user control. Concretely, we introduce user control around the input (i.e., user profile), process (i.e., recommendation algorithm), and output (i.e., recommendations) of the ERS. To evaluate our system, we conducted an online user study (N=30) to explore the impact of user control on users' perceptions of the ERS in terms of several important user-centric aspects. Moreover, we investigated the effects of user control on multiple recommendation goals, namely transparency, trust, and satisfaction, as well as the interactions between these goals. Our results demonstrate the positive impact of user control on user perceived benefits of the ERS. Moreover, our study shows that user control strongly correlates with transparency and moderately correlates with trust and satisfaction. In terms of interaction between these goals, our results reveal that transparency moderately correlates and trust strongly correlates with satisfaction. Whereas, transparency and trust stand out as less correlated with each other.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsSimilar Papers 제목 키워드 기반
ITM-Rec: An Open Data Set for Educational Recommender Systems
With the development of recommender systems (RS), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. However, the education domain may not benefit from these developments d…
Recommendation SystemsOverview on NLP Techniques for Content-based Recommender Systems for Books
Recommender systems are an essential part of today{'}s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-bas…
Recommendation SystemsThe ADAIO System at the BEA-2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues
This paper presents the ADAIO team's system entry in the Building Educational Applications (BEA) 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues. The task aims to assess the performance of st…
Few-Shot LearningResponse GenerationUser and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
Data is an essential resource for studying recommender systems. While there has been significant work on improving and evaluating state-of-the-art models and measuring various properties of recommender system outputs, le…
Collaborative FilteringDiversityFairnessRecommendation SystemsAdaptive Multi-Agent E-Learning Recommender Systems
Educational recommender systems have become a necessity in the recent years due to overload of available educational resource which makes it difficult for an individual to manually hunt for the required resource on the i…
Recommendation SystemsRetrieval