This study investigates the potential of automated classification using prompt-based learning approaches with transformer models (large language models trained in an unsupervised manner) for a domain-specific classification task. Prompt-based learning with zero or few shots has the potential to (1) make use of artificial intelligence without…

The employment rate of software developers has risen significantly over the last 30 years. As a result, more students are considering computer science as a potential career path. Over the last 15 years, introductory programming course (CS1) enrollment has been increasing at a much faster rate than the increase in the number of CS faculty, with no…

Text classification has allowed researchers to analyze natural language data at a previously impossible scale. However, a text classifier is only as valid as the the annotations on which it was trained. Further, the cost of training a classifier depends on annotators' ability to quickly and accurately apply the coding scheme to each text. Thus,…

As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…

Coding is a process of assigning meaning to a given piece of evidence. Evidence may be found in a variety of data types, including documents, research interviews, posts from social media, conversations from learning platforms, or any source of data that may provide insights for the questions under qualitative study. In this study, we focus on text…

This study discusses the automatic coding methods of the Community of Inquiry (CoI) framework for multilingual contexts, in particular. In universities, foreign students cannot be overlooked, and learning systems are also required to work in multilingual situations. However, none of the existing work has addressed the lack of language-agnostic and…

Research related to online discussions frequently faces the problem of analyzing huge corpora. Natural Language Processing (NLP) technologies may allow automating this analysis. However, the state-of-the-art in machine learning and text mining approaches yields models that do not transfer well between corpora related to different topics. Also,…

The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…

The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…