Teacher job satisfaction is an important research element in teacher education and educational research. Based on data from the China Education Tracking Survey, this study analyzes the factors that influence the job satisfaction of 690 junior high school teachers and further constructs a prediction model. The study shows that gender, burnout,…

The primary purpose of this study is to investigate the degree to which register knowledge, register-specific motivation, and diverse linguistic features are predictive of human judgment of writing quality in three registers--narrative, informative, and opinion. The secondary purpose is to compare the evaluation metrics of register-partitioned…

Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning--a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the…

Knowledge tracing (KT) is a method to evaluate a student's knowledge state (KS) based on their historical problem-solving records by predicting the next answer's binary correctness. Although widely applied to closed-ended questions, it lacks a detailed option tracing (OT) method for assessing multiple-choice questions (MCQs). This paper introduces…

Detecting wheel-spinning behaviors of students who interact with an Intelligent Tutoring System (ITS) is important for generating pertinent and effective feedback and developing more enriching learning experiences. This analysis compares decision tree and bagged tree models of student productive persistence (i.e., mastering a skill) using the…

Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond…

The early prediction of math difficulties (MD) is important as it facilitates timely support. MD are multifaceted, and several factors are involved in their manifestation. This makes the accurate early prediction of MD particularly challenging. In the present study, we aim to predict MD in Grade 6 with kindergarten-age (age 6) measures by applying…

This study examined the effects of a technology-enhanced intervention on the self-regulation of 262 eighth-grade students, employing information and communication technology (ICT) and web-based self-assessment tools set against science learning. The data were analyzed using Bayesian structural equation modeling to unravel the intricate…

A prior study developed the Computational Thinking Scale (CTS) for assessing individuals' computational thinking dispositions in five dimensions: decomposition, abstraction, algorithmic thinking, evaluation, and generalization. This study proposed the Developmental Model of Computational Thinking through validating the structural relationships…

Self-regulated learning (SRL) is a critical 21st -century skill. In this paper, we examine SRL through the lens of the searching, monitoring, assessing, rehearsing, and translating (SMART) schema for learning operations. We use microanalysis to measure SRL behaviors as students interact with a computer-based learning environment, Betty's Brain. We…

Game-based learning environments are designed to provide effective and engaging learning experiences for students. Predictive student models use trace data extracted from students' in-game learning behaviors to unobtrusively generate early assessments of student knowledge and skills, equipping game-based learning environments with the capacity to…

This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students' interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the…

This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined 1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction and 2)…

We explore 11-12-year-old students' emerging ideas of models and modelling as they engage in a data-modelling task involving inquiry based on data obtained from an experiment. We report on a design-based study in which students identified what and how to measure, decided how to structure and represent data, and made inferences and predictions…

Feature engineering, the construction of contextual and relevant features from system log data, is a crucial component of developing robust and interpretable models in educational data mining contexts. The practice of feature engineering depends on domain experts and system developers working in tandem in order to creatively identify actions and…