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The aim of this work is to provide data-driven insights regarding the factors behind dropouts in Higher Education and their impact over time. To this end, we analyzed students' data collected by a Higher Education Institute over the last 11 years and we explored how socio-economic and academic changes may have impacted student dropouts and how these changes may have been reflected or captured by students' data. To analyze the data, we engineered features that may predict student dropouts on three dimensions: academic background, students' performance and students' effort. Then we carried out a correlation analysis to investigate the potential relationship between these features and dropouts, we performed a multivariate analysis of variance (MANOVA) to investigate whether the engineered features change significantly among student cohorts with different admission year and, finally, we carried out a regression analysis to confirm that the engineered features' impact on predicting dropouts changes over the years. The results suggest that the importance of features regarding the academic background of students (such as the students' prior experience with the academic institution), and the effort students make (for example, the number of days students spend on academic leave) may change over time. On the contrary, performance-based features (such as credit points and grades) do interact with time suggesting that performance measures are stable predictors of dropouts over time. On the basis of the findings, we argue that the performance of prediction models for assessing students at risk of dropping out of their studies can be affected by the age of data and we outline the possibility of including a forgetting factor for non-recent data in order to leverage their impact on prediction performance. [For the full proceedings, see ED615472.]

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 these discussions, that is, domain contingent scaffolding and instructional contingent scaffolding, respectively. The pilot study compared an experimental and control version of Rimac. The experimental version uses students' responses to pretest items to initialize the student model and dynamically updates the model based on students' responses to tutor questions during reflective dialogues. It then decides what and how to discuss the next question based on the model predictions. The control version initializes its student model based on students' pretest performance but does not update the model further and assigns students to a fixed line of reasoning level based on the student model predictions. We hypothesized that students who used the experimental version of Rimac would achieve higher learning gains than students who used the control version. Although we did not find a significant difference in learning between conditions, the experimental group took significantly less time to complete the pilot study dialogues than did the control group. That is, the experimental condition led to more efficient learning, for both low and high prior knowledge level learners. We discuss this finding and describe future work to improve the tutor's potential to support student learning.

This paper discusses how a dialogue-based tutoring system makes decisions to proactively scaffold students during conceptual discussions about physics. The tutor uses a student model to predict the likelihood that the student will answer the next question in a dialogue script correctly. Based on these predictions, the tutor will, step by step, choose the granularity at which the next step in the dialogue is discussed. The tutor attempts to pursue the discussion at the highest possible level, with the goal of helping the student achieve mastery, but with the constraint that the questions it asks are within the student's ability to answer when appropriately supported; that is, the tutor aims to stay within its estimate of the student's zone of proximal development for the targeted concepts. The scaffolding provided by the tutor is further adapted by adjusting the way the questions are expressed.

In this paper, we propose a computational approach to model the Zone of Proximal Development (ZPD) using predicted probabilities of correctness and engaging students in reflective dialogue. To that end, we employ a predictive model that uses a linear function of a variety of parameters, including difficulty and student knowledge and we analyze the activity of students who use a natural language tutoring system that presents conceptual reflection questions after students solve high-school physics problems. In order to operationalize our approach, we introduce the concept of the "Grey Area", that is the area of uncertainty in which the student model cannot predict with acceptable accuracy whether a student is able to give a correct answer without support. We further discuss the impact of our approach on student modeling, the limitations of this work and we discuss future work in systematically and rigorously evaluating the approach.

In this paper, we propose a computational approach to modeling the Zone of Proximal Development of students who learn using a natural language tutoring system for physics. We employ a student model that predicts students' performance based on their prior knowledge and their activity when using a dialogue tutor to practice with conceptual, reflection questions about high-school physics. Furthermore, we introduce the concept of the "Grey Area", the area in which the student model cannot predict with acceptable accuracy whether a student has mastered the knowledge components or skills present in a particular step. We envision that our approach will contribute to the way we design learning content for ITSs and the way we author dialogues for natural-language tutoring systems. We further discuss the impact of our approach on student modeling and discuss future work in systematically and rigorously evaluating the approach.