This study explored the use of an AI tutor and its relationship to performance outcomes in a large introductory undergraduate STEM course, where the AI tutor was integrated into the online homework system. The course included 13 weekly homework assignments, comprising 221 questions that contributed 19.5% to the final grade. Results showed that students predominantly completed homework problems without AI tutor assistance, using it selectively to address specific challenges. Patterns of AI interaction varied at both the problem and student levels, with demographic factors having little to no relationship to AI usage. Notably, the frequency of AI use was not linked to exam performance. A multi-level cluster analysis identified distinct patterns in students’ use of the AI tutor during problem-solving. These patterns of use had more significant associations with performance than frequency of use alone. This paper explores these interaction patterns in depth and discusses the study’s limitations and implications.

Students’ reading is an essential part of learning in college courses. However, many instructors are concerned that students do not complete assigned readings, and multiple studies have found evidence to support this concern. A handful of studies suggest adopting strategies to address students’ lack of reading. This research examines various instructional strategies and student eTextbook reading behaviors validated by page view data. Survey responses related to use of instructional strategies were collected. A total of 86 instructors from four public universities participated. Of these participants, 59 submitted the assigned reading pages for their courses. This resulted in reading data from 3,714 students which were examined in this study. The findings indicated that students read about 37% of the assigned pages on any given day during the semester. Also, of the students that read, two-thirds made at least one annotation and students tend to re-read the pages they annotated. Most importantly, student reading in the courses where strategies were used was almost three times higher than in the courses where no strategies were implemented.

INTRODUCTION:The objective of the study was to compare a dental student's practical ability to detect and stage radiographic caries per International Caries Detection and Assessment System (ICDAS), following a traditional lecture and a lecture containing an interactive session using an audience response system (ARS). Associations between the order of instructions and student performance were also evaluated.MATERIALS AND METHODS:Eighty-three dental students were randomly assigned to groups A and B. On the first day, group A received a traditional lecture and group B received content using the ARS. All students then took an electronic quiz (T1) identifying and staging caries on radiographs per ICDAS. For the second day, group A received the content using the ARS system and group B received a traditional lecture. All students subsequently took a second electronic quiz (T2). Two survey questions about the learning experience were also included.RESULTS:Wilcoxon rank-sum analysis of scores from consenting students (81) showed no difference between the quiz 1 scores of two groups (p=.61). Whilst not statistically significant (p = .07), the group that had the ARS initially scored marginally higher on quiz 2. Survey results showed that most participants preferred either the ARS alone (49.38%) or a combination of the ARS and a traditional lecture (40.74%). A majority of them (80%) found the ARS helpful.CONCLUSION:When training students in practical skills of detection and staging radiographic presence of dental caries per ICDAS, hands-on learning tools, such as an ARS, complement traditional lectures.

Strategies that incorporate learning analytics are continuing to advance as higher education institutions work to promote student success. Although many of the early learning analytics applications were intended to help teaching professionals to identify at-risk students, some learning analytics applications display information on course progress and performance directly to students. While positive associations between student use of learning analytics and achievement have been reported, some have expressed concern that for at-risk students, low estimated grades might induce negative emotions, which could lead to disengagement or even withdrawal. However, few studies have examined the effects of such applications on at-risk students. Elements of Success is a learning analytics platform that provides students with weekly performance feedback, including a current estimated grade. This study examined the relationship between student use of Elements of Success and academic performance among at-risk students in an introductory chemistry course. Specifically, we compared final grade outcomes and the risk of withdrawal among students who received a low estimated grade after the first midterm. Results indicated that viewing performance feedback, including a low estimated grade, was not associated with withdrawal from the course for at-risk students. Furthermore, at-risk students who used Elements of Success were found to be resilient. After controlling for prior learning outcomes, demographics, and self-reported study skills, it was found that they were more likely to earn a final passing grade (C- or above) than at-risk students who never used it. The results and limitations are further discussed.

Instructional technologists and faculty in post-secondary institutions have increasingly adopted learning analytics interventions such as dashboards that provide real-time feedback to students to support student' ability to regulate their learning. But analyses of the effectiveness of such interventions can be confounded by measures of students' prior learning as well as their baseline level of self-regulated learning. For this research study, we sought to examine whether the frequency of accessing a dashboard was associated with learning outcomes after matching subjects on confounding variables. And because prior research has suggested that measures of prior learning are associated with students' likelihood to use learning analytics interventions, we sought to adequately control for learners' likelihood to access the feedback by using a propensity score matching with a non-binary treatment variable. We administered the Motivated Strategies for Learning Questionnaire and also collected demographic information for a propensity score matching process. Users' frequency of accessing the intervention was categorized as High, Moderate, or Low/No usage. After matching users on characteristics associated with dashboard usage (gender, high school GPA, and the "Test Anxiety" and "Self Efficacy" factors) we found that both the "High" and "Moderate" users achieved significantly higher course grades than the "Low/No" users. The results suggest learners benefited from regularly accessing the feedback, but extreme amounts of usage were not necessary to achieve a positive effect. We discuss the implications for recommending how students use learning analytics interventions without excessively accessing feedback.