相关论文: Human-Centered Development of Indicators for Self-…
Supporting student success requires collaboration among multiple stakeholders. Researchers have explored machine learning models for academic performance prediction; yet key challenges remain in ensuring these models are interpretable,…
The objectives of this ongoing research are to build Real-Time AI-Powered Educational Dashboard (RAED) as a decision support tool for instructors, and to measure its impact on them while making decisions. Current developments in AI can be…
A new generation of educational mathematics software is being shaped in ThEdu and other academic communities on the side of computer mathematics. Respective concepts and technologies have been clarified to an extent, which calls for…
The rapid integration of Large Language Models (LLMs) into software engineering practice is reshaping how software testing activities are performed. LLMs are increasingly used to support software testing. Consequently, software testing…
With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. In recent years, countries and regions such as China, the United States, and the European…
Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limited. Large language models (LLMs) present opportunities to…
The purpose of this paper is to determine potential identifiers of students' academic success in foundation mathematics course from the data logs of an intelligent tutor. A cross-sectional study design was used. A sample of 58 records was…
The identification and analysis of student satisfaction is a challenging issue. This is becoming increasingly important since a measure of student satisfaction is taken as an indication of how well a course has been taught. However, it…
While the advances in artificial intelligence and machine learning empower a new generation of autonomous systems for assisting human performance, one major concern arises from the human factors perspective: Humans have difficulty…
Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Hypothesizing that SSL models would learn more generic,…
Social science research increasingly demands data-driven insights, yet researchers often face barriers such as lack of technical expertise, inconsistent data formats, and limited access to reliable datasets.Social science research…
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning…
With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight students' (mis)use of LLMs can hinder learning outcomes.…
Educational e-book platforms provide valuable information to teachers and researchers through two main sources: reading activity data and reading content data. While reading activity data is commonly used to analyze learning strategies and…
This work examines the sociotechnical factors that influence the adoption and usage of intelligent tutoring systems in self-directed learning contexts, focusing specifically on adult learners. The study is divided into two parts. First, we…
As AI becomes more common in everyday living, there is an increasing demand for intelligent systems that are both performant and understandable. Explainable AI (XAI) systems aim to provide comprehensible explanations of decisions and…
The aim of the work presented in this paper is to develop and evaluate an integrated system that provides automated lecture style evaluation, allowing teachers to get instant feedback related to the goodness of their lecturing style. The…
Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In…
Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of…
Software analytics is a data-driven approach to decision making, which allows software practitioners to leverage valuable insights from data about software to achieve higher development process productivity and improve different aspects of…