A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.Comment: 25 pages, 5 figure

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Panorama of Recommender Systems to Support Learning

This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424). Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders (FWO). Olga C. Santos would like to acknowledge that her contributions to this work have been carried out within the project Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts (MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported with funding CIP-PSP Open Discovery Space (297229

A RECOMMENDER MODEL USING SOCIAL TIE STRENGTH FOR THE CHUNK LEARNING SYSTEM

With the onset of COVID-19, rising tuition costs, and technological advancements, online courses have become a pervasive medium through which education is conducted. Currently, several online educational services tailor education to students through various methods of recommender models. One such system, the Curated Heuristic Using a Network of Knowledge (CHUNK) Learning, developed at the Naval Postgraduate School, uses a recommender system that relies on user profile attributes. We propose a complementary recommendation system to expand upon CHUNK's current recommender method by incorporating implicit recommendations from a user's social network based on tie strength between learners. In this work, we create a synthetic social network of learners and calculate the Jaccard Index and Pearson Correlation Coefficient similarity values to distinguish between strong and weak social ties. These tie classifications are then used to personalize content recommendations and expose users to greater breadth or depth of applicable knowledge based on current interests or job goals. We simulate recommendations for a user under different circumstances and show that our recommender system promotes the algorithmic formation of communities of learners on similar educational tracks. This promotes the social-emotional support for online learners that they may not currently receive and improves socialization within distance learning.Ensign, United States NavyApproved for public release. Distribution is unlimited

A Survey on Fairness-aware Recommender Systems

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.Comment: 27 pages, 9 figure

Artificial Intelligence methodologies to early predict student outcome and enrich learning material

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education