Danielle A. Allessio

University of Massachusetts Amherst | UMass Amherst · College of Information and Computer Sciences

We localized a Mathematics Intelligent Tutoring System that addresses motivational aspects of learning to the language and culture of a Latin-American country, Argentina. We analyzed its impact after three different schools using the software for seven weeks in three schools in Cordoba, Argentina. Results yielded a significant improvement in mathem...

Analysis of the movements of the Mouse pointer could lead to valuable insights into a user’s mental status in digital environments. Previous research has yielded data showing a significant link between user mental status and pointer movements [1]. However, there is currently no standardized system to detect and parse out individual targeted movemen...

Despite the potential and emerging applications of large language models (LLMs) for education, little is known about their effectiveness in learning. Similarly, educators' preferences and perceptions on the utility of LLMs have received limited to no attention. Hence, we conducted an exploratory study to investigate pre-service teachers' perception...

A major challenge for online learning systems is supporting students’ engagement. Online systems are sometimes boring, repetitive, and unappealing; external distractions often lead to off-task behavior, and a decline in learning. Student engagement and emotion are also tightly correlated with learning gains because emotion drives attention and atte...

We propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS) by analyzing their faces and gestures. The ability to predict such outcomes enables tutoring systems to adjust interventions and ultimately yield improved student learning. We collected and released a lab...

Abstract— In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student’s face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning th...

While using online learning software, students demonstrate many reactions, various levels of engagement, and emotions (e.g. confusion, boredom, excitement). Having such information automatically accessible to teachers (or digital tutors) can aid in understanding how students are progressing, and suggest who and when needs further assistance. As par...

While using online learning software, students demonstrate many reactions, various levels of engagement, and emotions (e.g. confusion, excitement, frustration). Having such information automatically accessible to teachers (or digital tutors) can aid in understanding how students progress and suggest when and who needs further assistance. We develop...

In the context of building an intelligent tutoring system (ITS), which improves student learning outcomes by intervention, we set out to improve prediction of student problem outcome. In essence, we want to predict the outcome of a student answering a problem in an ITS from a video feed by analyzing their face and gestures. For this, we present a n...

We empirically investigate two methods for eliciting student emotion within an online instructional environment. Students may not fully express their emotions when asked to report on a single emotion. Furthermore, students’ usage of emotional terms may differ from that of researchers. To address these issues, we tested two alternative emotion self-...

Many intelligent tutors are not designed with English language learners (ELL) in mind. As a result, Hispanic ELL students, a large and underserved population in U.S. classrooms, may experience difficulty accessing the relevant tutor content. This research investigates how Hispanic and ELL students perceive the utility of and relate to animated peda...

We present results of a randomized controlled study that compared different types of affective messages delivered by pedagogical agents. We used animated characters that were empathic and emphasized the malleability of intelligence and the importance of effort. Results showed significant correlations between students who received more empathic mess...

Prior research indicates that students often experience negative emotions while using online learning environments, and that most of these negative emotions can have a detrimental impact on their behavior and learning outcomes. We investigate the impact of a particular intervention, namely face-to-face collaboration with a neighboring student, on s...

Students self-reported not only their emotional state, but also the causal attributions of their emotions. After coding emotions with internal references to self, and external references to the environment or domain, we examined how sub-groups of students based on internal/external attributions and above or below median performance differ in terms...

This work addresses students’ open responses on causal attributions of their self-reported affective states. We use qualitative thematic data analysis techniques to develop a coding scheme by identifying common themes in students’ self-reported attributions. We then applied this scheme to a larger set of student reports. Analysis shows that student...

We address empirical methods to assess the reliability and design of affective self-reports. Previous research has shown that students may have subjectively different understandings of the affective state they are reporting [18], particularly among younger students[10]. For example, what one student describes as "extremely frustrating" another migh...

In this paper we report the results of a study which investigated the affordances of multi-user virtual environments (MUVEs) for collaborative learning from a design perspective. Utilizing a mixed methods approach, we conducted a comparative study of the effect of varying representational and interactional design features on a collaborative design...