A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

In personalized learning, each student gets a customized learning plan according to their pace of learning, instructional preferences, learning objects, etc. Hence the content recommender system in Personalized Learning Environment (PLE) should adapt to learner attributes and suggest appropriate learning resources to aid the learning process and improve the learning outcomes. This systematic literature review aims to analyze and summarize the studies on learning content recommenders in adaptive and personalized learning environments from 2015 to 2020. The publications were searched using proper keywords and filtered using the inclusion and exclusion criteria, which resulted in 52 publications. This paper summarizes the recent trends in research on different aspects of the recommender systems, such as learner attributes, recommendation methods, evaluation metrics, and the usability tests used by the researchers. It is observed that cognitive aspects of learners like learning style, preferences, knowledge level, etc., are used by most studies than non-cognitive aspects as social tags or trust. In most cases, recommendation engines are a hybrid of collaborative filtering, content-based filtering, ontological approaches, etc. All models were evaluated for the correctness of the prediction done, and a few studies have also done evaluations based on learner satisfaction or usability.

Animations about Clinical Trial Participation for Cancer Patients and Survivors

Communicating about clinical trials and medical research is challenging. An appropriate communication is essential to reduce some of the barriers associated with poor patients' enrollment in clinical trials and with patients' uninformed consent or uninformed refusal. An experiment was conducted to assess the effects of educational animations compared to brochures with and without visuals, and with the materials currently used by the NIH. These materials focused on explaining placebos, randomization, the steps necessary to enroll in a clinical trial, and how and by who patients' protection is ensured. A total of 1194 cancer patients and survivors completed this 4 by 4 experiment through a Qualtrics panel. The findings showed that animations improved participants knowledge about and attitudes toward clinical trials and were more effective than brochures presenting information from the NIH, especially for those individuals with low motivation and low ability to comprehend health-related information. Several evidence-based theoretical explanations of the functioning of animations are provided.

Research on Multi-Source Data Integration Based on Ontology and Karma Modeling

The purpose of data integration is that integrates multi-source heterogeneous data. Ontology solves semantic describing of multi-source heterogeneous data. The authors propose a practical approach based on ontology modeling and an information toolkit named Karma modeling for fast data integration, and demonstrate an application example in detail. Armed Conflict Location & Event Data Project (ACLED) is a publicly available conflict event dataset designed for disaggregated conflict analysis and crisis mapping. The authors analyzed the ACLED dataset and domain knowledge to build an Armed Conflict Event ontology, then constructed Karma models to integrate ACLED datasets and publish RDF data. Through SPARQL query to check the correctness of published RDF data. Authors design and developed an ACLED Query System based on Jena API, Canvas JS, and Baidu API, etc. technologies, which provides convenience for governments and researches to analyze regional conflict events and crisis early warning, and it verifies the validity of constructed ontology and the correctness of Karma modeling.