Nile Lab announce a new publication in the international peer-reviewed journal Information (MDPI) entitled “A Hybrid Recommendation Approach for Adaptive Worksheet Generation Using Pedagogically Structured Learning Objects” authored by Iraklis Katsaris, Sakellaris Sfakiotakis, Ilias Logothetis, and Nikolas Vidakis.
The study introduces a hybrid recommendation approach for adaptive worksheet generation that integrates content-based and collaborative filtering techniques with explicit pedagogical constraints derived from Bloom’s Revised Taxonomy. The proposed system ranks and selects learning and evaluation objects across cognitive levels by combining learner profiles, behavioural data, and similarity-based metrics within a unified scoring framework. A simulation-based evaluation was conducted to assess the system’s behaviour, stability, and instructional alignment. The results demonstrate strong alignment with intended instructional structures at lower cognitive levels, controlled and interpretable adaptive variation at higher cognitive levels and high agreement (exceeding 95%) in evaluation object recommendations across all simulated conditions. The findings highlight the potential of hybrid recommender systems to support transparent, robust, and pedagogically grounded adaptive learning environments.
This work contributes to ongoing research of Nile Lab in adaptive learning, recommender systems, and information-driven educational technologies, reinforcing Nile Lab commitment to innovation in teaching and learning.
The full open access article is available at: https://www.mdpi.com/2078-