A Personalized Task Recommendation System for Vocabulary Learning Based on Readability and Diversity

Xie, Haoran; Wang, Minhong; Zou, Di; Wang, Fu Lee

doi:10.1007/978-3-030-21562-0_7

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…Recommendation systems have been proposed in classroom and educational settings. Xie and colleagues focused on how to integrate personalization techniques to a recommendation system for second language vocabulary learning, focusing on the diversity and readability of the recommendations [32]. Another study analyzed the challenges of a recommendation system for reading materials in a classroom setting which focused on the individual student differences both in preferences and comprehension abilities [33].…”

Section: Persuasion and Explainability In Recommendations For Childrenmentioning

confidence: 99%

BrainHood: Designing a cognitive training system that supports self-regulated learning skills in children

Tsiakas¹,

Barakova²,

Khan³

et al. 2020

TAD

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BACKGROUND: There is strong evidence that cognitive skills and executive functions are skills that children need in order to successfully learn in school. Although executive function disorders are not considered a learning disability, weaknesses in executive functioning are often observed in students with learning disabilities or ADHD. Cognitive games are a type of educational games which focus on enhancing cognitive functioning in children with different profiles of cognitive development, including students with neurocognitive and/or learning disabilities. Self-regulation and metacognitive skills also play an important role in academic performance. OBJECTIVE: In this work, we highlight the need of monitoring and supporting metacognitive skills (self-regulation) in the context of a cognitive training game. We propose a system for self-regulated cognitive training for children which supports metacognitive strategies allowing the child to reflect on their own progress, weaknesses and strengths, self-arrange the training content, and thus to promote their self-regulated learning skills. METHODS: We provide a narrative review of research in cognitive training, self-regulated learning and explainable recommendation systems for children in educational settings. RESULTS AND CONCLUSIONS: Based on the review, an experimental testbed is proposed to explore how transparency, explainability and persuasive strategies can be used to promote self-regulated learning skills in children, considering individual differences on learning abilities, preferences, and needs.

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Section: Persuasion and Explainability In Recommendations For Childrenmentioning

confidence: 99%

BrainHood: Designing a cognitive training system that supports self-regulated learning skills in children

Tsiakas¹,

Barakova²,

Khan³

et al. 2020

TAD

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“…The latest RS application in personalized education was developed by Xie et al [29], who integrated the RS into the computer science field to learn English. This PLE involved the content-based (CB) technique, which aimed to identify the most similar items based on the learner's personalized model.…”

Section: Discussionmentioning

confidence: 99%

Section: Theme 1: Type Of Studymentioning

confidence: 99%

“…Ten articles came from the empirical research that was conducted from 2007 until 2020 [20]- [29]. Seven of these ten papers revealed the creation of RS and its integration to PLE [20], [21], [25]- [29], and six of these seven articles employed artificial intelligence (AI) methods that were applied in the development of RS [20], [25]- [29]. There were also two researchers who reported the utilization of AI methods in their investigated learning platform [22], [23], and lastly, three other groups of researchers evaluated the current personalized recommender system [24]- [26].…”

Section: Theme 1: Type Of Studymentioning

confidence: 99%

“…The second theme emerged in 15 papers, eight from the empirical study [20], [22], [23], [25]- [29], and seven in the literature review [30], [31], [34], [36]- [39]. Table III classifies AI techniques used by RS that were employed and discussed in the analyzed papers.…”

Section: Theme 1: Type Of Studymentioning

confidence: 99%

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Exploring the Potential of Deep Learning for Personalized Learning Environments

Junus,

Brophy

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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This study investigates the current potential for artificial intelligence (AI) to support personalized learning (PL). Personalized learning can provide a customized learning environment to support student learning processes based on individual needs, competencies, and interests. One way to conduct personalized learning is by using a recommender system that employs deep learning, an AI technique. To date, a limited number of researchers have discussed the application of deep learning methods to develop advanced recommenders in personalized learning environments. This study examines the literature that describes deep learning as a recommender system to support personalized learning environments. This initial phase of the project seeks to synthesize the issues and opportunities associated with personalized learning experiences and the potential of using deep learning to support the process. Because the topic intersects the education and information technology (IT) fields, we selected three databases for this literature review project: Scopus, ERIC, and Engineering Village. We used the phrase "deep learning recommender system for personalized learning environments" as our search string. We focused only on papers that experts had evaluated in the field to ensure accuracy. Therefore, terms such as "peer review," "literature review," and "systematic review" were added to the original search string. The initial search results included 409 documents. After applying inclusion/exclusion criteria, 20 papers emerged as the focus of this study. Thematic analysis was used to look for various themes to identify how deep learning methods are used in education and their potential to inform personalized learning environments. The analysis process utilized Mendeley and NVivo to quickly capture themes by focusing on six features to peruse within the articles. The features involved research questions, goals of studies, research methodology, research design, primary outcomes, and limitations. We then generated three themes from the six features in the analysis phase of the 20 papers. The first theme categorized the type of study into primary and secondary studies. These categories identify the types of studies that employed deep learning methods in the development of a recommender system and their integration with personalized learning. The second theme, recommender system (RS) techniques, highlighted the AI methodologies most frequently utilized in previous research. And the third theme was a list of e-learning platforms that applied RS for personalized learning. The main findings revealed that the deep learning method was effective in big data analysis due to its ability to forecast students' achievements, behaviors, and future paths. Therefore, we considered that deep learning could be widely applied as a technique to develop recommender systems to support personalized learning environments. Furthermore, because we found that only a few studies have investigated the implementation of this AI technology, researchers will have a great...

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