Teacher classroom teaching behavior indicators serve as a crucial foundation for guiding instructional evaluation. Existing indicator system suffers from limitations such as strong subjectivity and weak contextual generalization capabilities. Generalized category discovery (GCD) enables automatic data clustering to identify known categories and…

Increased use of computer-based assessments has facilitated data collection processes that capture both response product data (i.e., correct and incorrect) and response process data (e.g., time-stamped action sequences). Evidence suggests a strong relationship between respondents' correct/incorrect responses and their problem-solving proficiency…

In this ethnographic study, I present a single school's practice of registering and analysing absence from school. I show that teachers use various "dirty," interpretational contexts for understanding absence and make it classifiable in "clean" attendance categories -- a move that decontextualises the meaning of absence. When…

Many innovations that are implemented in schools are initially successful, but fail to become part of the schools' habits and routines. Relatively little research has followed innovations in schools for a long(er) time. In addition, few reforms last long enough to be studied longitudinally. In this exploratory study, the authors aim to find a way…

This study investigates how 11- to 12-year-old students construct data-based decision trees using data cards for classification purposes. We examine the students' heuristics and reasoning during this process. The research is based on an eight-week teaching unit during which students labeled data, built decision trees, and assessed them using test…

We report findings of this replication study, examining the internalizing subscale (SRSS-I4) of the revised version of the Student Risk Screening Scale for Internalizing and Externalizing behavior (SRSS-IE 9) and the internalizing subscale of the Teacher Report Form (TRF). Using the sample from 13 elementary schools across three U.S. states with…

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery (SD) approach based on MapReduce. The proposed SD approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope…

We report the results of a convergent validity study examining the externalizing subscale (SRSS-E5, five items) of the adapted Student Risk Screening Scale for Internalizing and Externalizing (SRSS-IE 9) with the externalizing subscale of the Teacher Report Form (TRF) with two samples of K-12 students. Results of logistic regression and receiver…

The question: "What is an appropriate role for AI?" is the subject of much discussion and interest. Arguments about whether AI should be a "human replacing" technology or a "human assisting" technology frequently take centre stage. Education is no exception when it comes to questions about the role that AI should…

This study uses the log data from Moodle learning management system for predicting student learning performance in the first third of a semester. Since the quality of the data has great influence on the accuracy of machine learning, five major data transmission methods are used to enhance data quality of log file in the data preprocessing stage.…

In April 2021, the Midwestern Higher Education Compact (MHEC) convened a working group of institution, state, and national leaders to help advise its efforts to develop a set of principles to improve consistency and reliability in the field for measuring cost savings and the return on investment (ROI) of open education resources (OER). The result…

Many of the analytical models commonly used in educational research often aim to maximize explained variance and identify variable importance within models. These models are useful for understanding general ideas and trends, but give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method rooted in…

The purpose of this study is to describe the politeness approach of the Japanese-Balinese family realm language. The study explores how the use of language politeness strategies is affected by the cultural background of both spouses in intercultural marriages. The study also examines the various factors that influence language politeness…

Effectively estimating student enrollment and recruiting students is critical to the success of any university. However, despite having an abundance of data and researchers at the forefront of data science, traditional universities are not fully leveraging machine learning and data mining approaches to improve their enrollment management…

In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…