Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners

This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video clickstream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engagement over time. Grounded in the interdisciplinary field of network science, we follow a graph based approach to successfully extract indicators of active and passive MOOC participation that reflect persistence and regularity in the overall interaction footprint. Using these rich educational semantics, we focus on the problem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing "attrition intensifying" features from the learning activities that MOOC learners engage in. Implications for some compelling future research are discussed.Comment: "Shared Task" submission for EMNLP 2014 Workshop on Modeling Large Scale Social Interaction in Massively Open Online Course

Reconciling Contemporary Approaches to School Attendance and School Absenteeism: Toward Promotion and Nimble Response, Global Policy Review and Implementation, and Future Adaptability (Part 1)

School attendance is an important foundational competency for children and adolescents, and school absenteeism has been linked to myriad short- and long-term negative consequences, even into adulthood. Many efforts have been made to conceptualize and address this population across various categories and dimensions of functioning and across multiple disciplines, resulting in both a rich literature base and a splintered view regarding this population. This article (Part 1 of 2) reviews and critiques key categorical and dimensional approaches to conceptualizing school attendance and school absenteeism, with an eye toward reconciling these approaches (Part 2 of 2) to develop a roadmap for preventative and intervention strategies, early warning systems and nimble response, global policy review, dissemination and implementation, and adaptations to future changes in education and technology. This article sets the stage for a discussion of a multidimensional, multi-tiered system of supports pyramid model as a heuristic framework for conceptualizing the manifold aspects of school attendance and school absenteeism

From participation to dropout

The academic e-learning practice has to deal with various participation patterns and types of online learners with different support needs. The online instructors are challenged to recognize these and react accordingly. Among the participation patterns, special attention is requested by dropouts, which can perturbate online collaboration. Therefore we are in search of a method of early identification of participation patterns and prediction of dropouts. To do this, we use a quantitative view of participation that takes into account only observable variables. On this background we identify in a field study the participation indicators that are relevant for the course completion, i.e. produce significant differences between the completion and dropout sub-groups. Further we identify through cluster analysis four participation patterns with different support needs. One of them is the dropout cluster that could be predicted with an accuracy of nearly 80%. As a practical consequence, this study recommends a simple, easy-to-implement prediction method for dropouts, which can improve online teaching. As a theoretical consequence, we underline the role of the course didactics for the definition of participation, and call for refining previous attrition models

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

This article belongs to the Special Issue Smart LearningIn education, several studies have tried to track student persistence (i.e., students' ability to keep on working on the assigned tasks) using di fferent definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students' interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students' profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly a ffected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students' average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.This work was partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/project Smartlet (TIN2017-85179-C3-1-R), and by the Madrid Regional Government, through the project e-Madrid-CM (S2018/TCS-4307). The latter is also co-financed by the Structural Funds (FSE and FEDER). This work received also partial support by Ministerio de Ciencia, Innovación y Universidades, under an FPU fellowship (FPU016/00526)

Ripple: Concept-Based Interpretation for Raw Time Series Models in Education

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extracting these features is labor-intensive for humans and computers. In this paper, we propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy with raw time series clickstreams in comparison to hand-crafted features. Furthermore, we extend concept activation vectors for interpretability in raw time series models. We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. Our experimental analysis on 23 MOOCs with millions of combined interactions over six behavioral dimensions show that models designed with our approach can (i) beat state-of-the-art educational time series baselines with no feature extraction and (ii) provide interpretable insights for personalized interventions. Source code: https://github.com/epfl-ml4ed/ripple/.Comment: Accepted as a full paper at AAAI 2023: 37th AAAI Conference on Artificial Intelligence (EAAI: AI for Education Special Track), 7-14 of February 2023, Washington DC, US

Dropout, persistence, and retention on online higher education: The case of the Open University of Catalonia

This doctoral thesis focuses on student dropout in online higher education (OHE), with a focus on the experience of students and faculty at the Universitat Oberta de Catalunya (UOC). The thesis is presented as a compendium of six publications that address issues related to dropout, persistence and retention in OHE. A qualitative and exploratory descriptive research design is used that includes in-depth open interviews and qualitative content analysis. The findings highlight the importance of the first year for dropout and persistence, with time-related factors being the main barriers to persistence and reasons for dropout. In addition, persistent students presented different characteristics and dynamics compared to students who dropped out. Various theoretical and practical implications derived from the integrated findings are discussed, including a series of practical recommendations and possible interventions.Esta tesis doctoral se centra en el abandono de estudiantes en la educación superior en línea (ESL), con un enfoque en la experiencia de los estudiantes y profesores en la Universitat Oberta de Catalunya (UOC). La tesis se presenta como un compendio de seis publicaciones que abordan temas relacionados con el abandono, la persistencia y la retención en la ESL. Se utiliza un diseño de investigación cualitativo y exploratorio-descriptivo que incluye entrevistas abiertas en profundidad y análisis de contenido cualitativo. Los hallazgos destacan la importancia del primer año para el abandono y la persistencia, con factores relacionados con el tiempo como las principales barreras para la persistencia y razones para el abandono. Además, los estudiantes persistentes presentan características y dinámicas distintas en comparación con los estudiantes que abandonan. Se discuten varias implicaciones teóricas y prácticas derivadas de los hallazgos integrados, incluyendo una serie de recomendaciones prácticas y posibles intervenciones.Aquesta tesi doctoral se centra en l'abandó d'estudiants a l'educació superior en línia (ESL), amb un enfocament a l'experiència dels estudiants i professors a la Universitat Oberta de Catalunya (UOC). La tesi es presenta com un compendi de sis publicacions que aborden temes relacionats amb l'abandó, la persistència i la retenció a l'ESL. S'utilitza un disseny de recerca qualitatiu i exploratori-descriptiu que inclou entrevistes obertes en profunditat i anàlisi de contingut qualitatiu. Els resultats destaquen la importància del primer any per a l'abandó i la persistència, amb factors relacionats amb el temps com les barreres principals per a la persistència i raons per a l'abandó. A més, els estudiants persistents presenten característiques i dinàmiques diferents en comparació amb els estudiants que abandonen. Es discuteixen diverses implicacions teòriques i pràctiques derivades dels resultats integrats, incloent-hi una sèrie de recomanacions pràctiques i possibles intervencions.e-learnin