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 the Evolution of Language Model-Based Dialogue Systems

Dialogue systems, including task-oriented_dialogue_system (TOD) and open-domain_dialogue_system (ODD), have undergone significant transformations, with language_models (LM) playing a central role. This survey delves into the historical trajectory of dialogue systems, elucidating their intricate relationship with advancements in language models by categorizing this evolution into four distinct stages, each marked by pivotal LM breakthroughs: 1) Early_Stage: characterized by statistical LMs, resulting in rule-based or machine-learning-driven dialogue_systems; 2) Independent development of TOD and ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack intrinsic knowledge in their parameters; 3) fusion between different types of dialogue systems with the advert of pre-trained_language_models (PLMs), starting from the fusion between four_sub-tasks_within_TOD, and then TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be used to conduct TOD and ODD seamlessly. Thus, our survey provides a chronological perspective aligned with LM breakthroughs, offering a comprehensive review of state-of-the-art research outcomes. What's more, we focus on emerging topics and discuss open challenges, providing valuable insights into future directions for LLM-based_dialogue_systems. Through this exploration, we pave the way for a deeper_comprehension of the evolution, guiding future developments in LM-based dialogue_systems

A STUDY OF THE EFFECTIVENESS OF IXL MATH ONLINE SOFTWARE ON STUDENT ACHIEVEMENT IN AN URBAN MIDDLE SCHOOL

The purpose of this study is to evaluate the effectiveness of IXL Math online software in raising student achievement on the New York State Math exam, with special focus on effects by student gender, ethnicity and disability status. The study includes an analysis of the relationship between scale scores and time spent using the IXL system, number of problems attempted, and skills mastered. This study is significant because national, state and local measures indicate no measurable improvement in math achievement with an alarming percentage of students scoring below proficient levels. Further, past studies examined teacher perception or student motivation regarding educational technology and achievement. To date, is the only study independently analyzing the effectiveness of a widely-used online learning application, IXL Math, in a Title 1 urban Middle School consisting of 6th, 7th and 8th grade students and measuring the impact of the online program on students most at-risk, and whose attributes comprise the lowest third percentile of achievers. A quasi-experimental research design was conducted by comparing two distinct cohorts of students – one using traditional paper assignments and the other completing IXL online assignments, and using statistical analysis, a determination was made that there was no significant difference in the scale scores between the two groups. Additionally, the interaction between IXL and gender, ethnicity, and disability, and its effect on math scores was analyzed. Two-way analysis of variance tests and Pearson’s correlation were conducted using SPSS software package. The major findings are discussed offering recommendations for future practice and research

Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning

Computerized adaptive testing (CAT) can effectively facilitate student assessment by dynamically selecting questions on the basis of learner knowledge and item difficulty. However, most CAT models are designed for one-time evaluation rather than improving learning through formative assessment. Since students cannot remember everything, encouraging them to repeatedly evaluate their knowledge state and identify their weaknesses is critical when developing an adaptive formative assessment system in real educational contexts. This study aims to achieve this goal by proposing an adaptive formative assessment system based on CAT and the learning memory cycle to enable the repeated evaluation of students' knowledge. The CAT model measures student knowledge and item difficulty, and the learning memory cycle component of the system accounts for students’ retention of information learned from each item. The proposed system was compared with an adaptive assessment system based on CAT only and a traditional nonadaptive assessment system. A 7-week experiment was conducted among students in a university programming course. The experimental results indicated that the students who used the proposed assessment system outperformed the students who used the other two systems in terms of learning performance and engagement in practice tests and reading materials. The present study provides insights for researchers who wish to develop formative assessment systems that can adaptively generate practice tests

Minds Online: The Interface between Web Science, Cognitive Science, and the Philosophy of Mind

Alongside existing research into the social, political and economic impacts of the Web, there is a need to study the Web from a cognitive and epistemic perspective. This is particularly so as new and emerging technologies alter the nature of our interactive engagements with the Web, transforming the extent to which our thoughts and actions are shaped by the online environment. Situated and ecological approaches to cognition are relevant to understanding the cognitive significance of the Web because of the emphasis they place on forces and factors that reside at the level of agent–world interactions. In particular, by adopting a situated or ecological approach to cognition, we are able to assess the significance of the Web from the perspective of research into embodied, extended, embedded, social and collective cognition. The results of this analysis help to reshape the interdisciplinary configuration of Web Science, expanding its theoretical and empirical remit to include the disciplines of both cognitive science and the philosophy of mind