As education increasingly shifts towards a technology-driven model, artificial intelligence systems like ChatGPT are gaining recognition for their potential to enhance educational support. In university education and MOOC environments, students often select courses that align with their specific needs. During this process, access to information…

Intelligent tutoring systems (ITS) can help students learn successfully, yet little work has explored the role of caregivers in shaping that success. Past interventions to support caregivers in supporting their child's homework have been largely disjunct from educational technology. The paper presents prototyping design research with nine middle…

Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers. However, manually labeling educational content is labor intensive and error-prone. To address this challenge, prior research proposed machine learning based solutions to auto-label educational content with limited success.…

Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…

The expanding domain of Artificial Intelligence (AI) offers a diverse array of educational applications and tools. However, the scholarly exploration of AI's suitability for enhancing English as a Foreign Language (EFL) instruction at the university level remains notably limited. This research gap impedes educators from fully harnessing AI's…

Learning from text is a "constructive" activity in which sentence-level information is combined by the reader to build coherent mental models. With increasingly complex texts, forming a mental model becomes challenging due to a lack of background knowledge, and limits in working memory and attention. To address this, we are taught…

Conversational Intelligent Tutoring Systems (ITSs) are expensive to develop. While simple online courseware could be easily authored by teachers, the authoring of conversational ITSs usually involves a team of experts with different expertise, including domain experts, linguists, instruction designers, programmers, artists, computer scientists,…

This research evaluates the psychometric properties of short-answer response items under a variety of grading rules in the context of a mobile learning platform in Africa. This work has three main findings. First, we introduce the concept of a differential device function (DDF), a type of differential item function that stems from the device a…

A key benefit in intelligent Computer Assisted Language Learning (iCALL) is that it allows complex linguistic phenomena to be incorporated into digital learning platforms, either for the autonomous learner or to complement classroom teaching. The present paper describes (1) complex phonological/ morphophonemic alternations of Irish, which are…

This paper describes an initial pilot study of Rimac, a natural-language tutoring system for physics. Rimac uses a student model to guide decisions about "what content to discuss next" during reflective dialogues that are initiated after students solve quantitative physics problems, and "how much support to provide" during…

Presentation of raw text to language learners is not enough to ensure learning. Thus, we present the Smart and Immersive Language Learning Environment (SMILLE), a system that uses Natural Language Processing (NLP) for enhancing grammatical information in texts chosen by a given user. The enhancements, carried out by means of text highlighting, are…

An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict…

A number of studies have demonstrated links between linguistic knowledge and performance in math. Studies examining these links in first language speakers of English have traditionally relied on correlational analyses between linguistic knowledge tests and standardized math tests. For second language (L2) speakers, the majority of studies have…

This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and…

In the current study, we utilize natural language processing techniques to examine relations between the linguistic properties of students' self-explanations and their reading comprehension skills. Linguistic features of students' aggregated self-explanations were analyzed using the Linguistic Inquiry and Word Count (LIWC) software. Results…