Recently, the rise of generative AI tools such as "ChatGPT" have prompted deep and wide considerations about teaching and learning, student success, research and development, and the use of data for informed institutional decision making. In this volume, authors discuss specific concepts, considerations for use, and some specific tools…

Integrating machine learning (ML) methods in educational research has the potential to greatly impact upon research, teaching, learning and assessment by enabling personalised learning, adaptive assessment and providing insights into student performance, progress and learning patterns. To reveal more about this notion, we investigated ML…

This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…

This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN…

Generative artificial intelligence (GenAI) has opened new possibilities for designing learning analytics (LA) tools, gaining new insights about student learning processes and their environment, and supporting teachers in assessing and monitoring students. This systematic literature review maps the empirical research of 41 papers utilizing GenAI…

This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…

This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…

Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…

Generative AI tools (GenAI) are increasingly used for academic tasks, including qualitative data analysis for the Scholarship of Teaching and Learning (SoTL). In our practice as academic developers, we are frequently asked for advice on whether this use for GenAI is reliable, valid, and ethical. Since this is a new field, we have not been able to…

Data mining has been successfully and widely utilized in educational information systems, and an important research field has been formed, which is educational data mining. Process mining inherits the characteristics of data mining which can not only use historical data in the system to analyze learning behavior and predict academic performance,…

In this essay, we explored the feasibility of utilizing artificial intelligence (AI) for qualitative data analysis in equity-focused research. Specifically, we compare thematic analyses of interview transcripts conducted by human coders with those performed by GPT-3 using a zero-shot chain-of-thought prompting strategy. Our results suggest that…

This study explores the use of generative AI, specifically ChatGPT, in statistical data analysis and its implications for statistics education at universities of applied sciences. This paper begins with first discussing the future division of labor between humans and machines in the context of statistical data analyses following the widespread…

Although there is growing social science research examining how generative AI models can be effectively and systematically applied to text-based tasks, whether and how these models can be used to analyze images remain open questions. In this article, we introduce a framework for analyzing images with generative multimodal models, which consists of…

It is common to find mixed-format data results from the use of both multiple-choice (MC) and constructed-response (CR) questions on assessments. Dealing with these mixed response types involves understanding what the assessment is measuring, and the use of suitable measurement models to estimate latent abilities. Past research in educational…

Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…