{'fraction_non_alphanumeric': 0.018561484918793503, 'fraction_numerical': 0.0, 'mean_word_length': 5.374384236453202, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Perfect Match: Facilitating Study Partner Matching

With the massive growth of online learning, there has been a decrease in students' face-to-face interactions, leading to rising feelings of isolation. This in turn contributes to several issues such as motivation loss, increased course attrition rates and poor learning experiences. Strong Online Learning Communities (OLCs) have been suggested as a means to help improve the situation, however the formation of OLCs is strongly influenced by learners' individual characteristics and their preferences regarding how and with whom they would want to form study groups. Taking students as its focus, this research attempts to develop a learning partner recommender system (LPRS) to facilitate finding compatible study peers in order to promote informal learning communities among students. From a synthesis of related literature and using data from a study of the student' preferences, a collection of learners' individual characteristics has been identified as a set of matching criteria in our LPRS model. A proof of concept based on the conceptual model has been developed and evaluated with a small group of target users. Results of the investigation showed positive feedback from participants and good prospects of the recommender system.