Early Dropout Prediction for Programming Courses Supported by Online Judges

Many educational institutions have been using online judges in programming classes, amongst others, to provide faster feedback for students and to reduce the teacher’s workload. There is some evidence that online judges also help in reducing dropout. Nevertheless, there is still a high level of dropout noticeable in introductory programming classes. In this sense, the objective of this work is to develop and validate a method for predicting student dropout using data from the first two weeks of study, to allow for early intervention. Instead of the classical questionnaire-based method, we opted for a non-subjective, data-driven approach. However, such approaches are known to suffer from a potential overload of factors, which may not all be relevant to the prediction task. As a result, we reached a very promising 80% of accuracy, and performed explicit extraction of the main factors leading to student dropout

MOOC next week dropout prediction: weekly assessing time and learning patterns

Although Massive Open Online Course (MOOC) systems have become more prevalent in recent years, associated student attrition rates are still a major drawback. In the past decade, many researchers have sought to explore the reasons behind learner attrition or lack of interest. A growing body of literature recognises the importance of the early prediction of student attrition from MOOCs, since it can lead to timely interventions. Among them, most are concerned with identifying the best features for the entire course dropout prediction. This study focuses on innovations in predicting student dropout rates by examining their next-week-based learning activities and behaviours. The study is based on multiple MOOC platforms including 251,662 students from 7 courses with 29 runs spanning in 2013 to 2018. This study aims to build a generalised early predictive model for the weekly prediction of student completion using machine learning algorithms. In addition, this study is the first to use a ‘learner’s jumping behaviour’ as a feature, to obtain a high dropout prediction accuracy

Capturing Fairness and Uncertainty in Student Dropout Prediction – A Comparison Study

This study aims to explore and improve ways of handling a continuous variable dataset, in order to predict student dropout in MOOCs, by implementing various models, including the ones most successful across various domains, such as recurrent neural network (RNN), and tree-based algorithms. Unlike existing studies, we arguably fairly compare each algorithm with the dataset that it can perform best with, thus ‘like for like’. I.e., we use a time-series dataset ‘as is’ with algorithms suited for time-series, as well as a conversion of the time-series into a discrete-variables dataset, through feature engineering, with algorithms handling well discrete variables. We show that these much lighter discrete models outperform the time-series models. Our work additionally shows the importance of handing the uncertainty in the data, via these ‘compressed’ models

Estrategia basada en la metodología Computer-Supported Collaborative Learning para la formación de grupos de trabajo automáticos en un curso de introducción a la programación (CS1)

Los cursos de Introducción a la programación presentan bajas calificaciones de los estudiantes, esto se refleja en las altas tasas de mortalidad y deserción académica. En este sentido, buscando formas de mejorar y apoyar el rendimiento académico de los estudiantes del curso CS1 - Fundamentos de Programación Orientada a Objetos (FPOO), este artículo propone una estrategia basada en la metodología Computer-Supported Collaborative Learning (CSCL) apoyada por un algoritmo para la formación de grupos de trabajo automáticos, que busca motivar a los estudiantes y permite adquirir conocimientos de forma homogénea en el desarrollo de actividades de programación. Bajo el marco del diseño cuasi experimental, se implementó la estrategia para diferentes actividades evaluativas en el curso FPOO, que permitió responder cuestiones relacionadas con la mejora de la calificación final de un estudiante utilizando la formación de grupos de trabajo automáticos en comparación a la formación de grupos de trabajo tradicional, y los resultados que se generan en las calificaciones cuando se desarrollan actividades sin formación de grupos. Los experimentos de este trabajo demuestran que el uso de la estrategia de colaboración mejora las calificaciones de los estudiantes en 22% en laboratorios y 20% en el proyecto final. Además, permite intercambiar conocimientos para resolver una tarea de programación. Finalmente, en este trabajo se concluye que el desarrollo de estrategias que integran la colaboración impacta positivamente en el proceso de aprendizaje de programación, mejorando significativamente las calificaciones del estudiante y las habilidades interpersonales que incentivan a mejorar el aprendizaje en los cursos de programación

Understanding novice programmer behavior on introductory courses - Learning analytics approach

It is not easy to learn programming. This is why increasing theoretical and practical knowledge in programming education benefits both the educators as well as the students. To allow the students to gain maximal benefit from their studies, the educator must be able to recognize the students who are struggling with learning programming. Learning analytics provides a possible solution to this problem. This thesis demonstrates a novel method to model programmer behavior by using Markov Models. Programming fulfills the Markov property, because the success of the next attempt to compile or execute code is not influenced by the previous attempts; only by the current skill level of the programmer. The model is built using a state machine, which consists of states representing the different phases of the programming process. The state machine contains eight different states and 29different state transition possibilities. A Markov chain corresponding to a specific student can be computed using this state machine and then used with, for example machine learning algorithms. The data for this thesis was collected from a total of five different introductory programming courses, which used either the Java or Python programming languages. The dataset contains 1174 unique students, who made 544 835 total submissions to411 unique assignments. All programming courses were given in Turku, during2017-2021.This thesis provides a theoretical basis for modeling students (Markov Models) and offers a practical method to model students using Markov Models. This thesis only applies unsupervised machine learning methods to the data, specifically the K-Means clustering algorithm. However, supervised methods may also be used. The usefulness of the model is demonstrated by clustering students into three statistically similar clusters: students who perform well, average and poorly. The model is also applied to recognize the programming language used, based only on the transitions within the state machine.--- Ohjelmoinnin oppiminen ei ole helppoa. Tästä syystä ohjelmoinnin opetuksenteoreettinen ja käytännön edistäminen hyödyttää paitsi nykyisin ohjelmointia opettavia, myös opiskelijoita. Jotta opiskelijat voivat saavuttaa maksimaalisenhyödyn opiskelustaan, opettajan täytyy voida tunnistaa ne opiskelijat, joille ohjelmoinnin opiskelu tuottaa hankaluuksia. Oppimisanalytiikka tarjoaa tähän mahdollisuuden. Tämä väitöskirja esittelee tavan mallintaa ohjelmoinnin opiskelijoidenkäyttäytymistä käyttämällä Markovin malleja. Ohjelmoijan käyttäytyminen toteuttaa Markovin ominaisuuden, sillä ohjelmoijan koodin ajoyrityksen onnistumiseen vaikuttaa ainoastaan ohjelmoijan senhetkinen taitotaso; aikaisemmilla yrityksillä ei ole vaikutusta tuleviin kertoihin. Malli rakennetaan käyttämällä tilakonetta, jonka jokainen tila vastaa ohjelmointiprosessin vaihetta. Tilakoneessa on yhteensä kahdeksan eri tilaa ja 29 erilaista tilan muutosmahdollisuutta. Tilakoneesta lasketaan opiskelijaa vastaava Markovin ketju, mitä voidaan käyttää esimerkiksi koneoppimisalgoritmien kanssa. Dataa tähän väitöskirjaan kerättiin yhteensä viidestä ohjelmoinninperuskurssista, joissa käytettiin joko Java- tai Python-ohjelmointikieltä. Opiskelijoita kursseilla oli yhteensä 1174. Opiskelijat tekivät yhteensä 544-835 ohjelmointitehtävän palautusta 411 ohjelmointitehtävään. Kaikki ohjelmointikurssit pidettiin Turussa, vuosina 2017-2021 Tämä väitöskirja tarjoaa teoreettisen pohjan ohjelmoinnin opiskelijoidenmallintamiseen (Markovin mallit) ja tarjoaa menetelmän, jolla Markovin malleja käyttämällä voi mallintaa ohjelmoinnin opiskelijoita. Malliin sovelletaan vain ohjaamattomia koneoppimismenetelmiä, erityisesti K-Means clustering -algoritmia. Tässä väitöskirjassa osoitan myös teoreettisen mallin muutamia käytännönsovelluksia luokittelemalla opiskelijoita samoja ominaisuuksia sisältäviin luokkiin. Malli opetetaan erottelemaan opiskelijat kolmeen ryhmään: hyvin, keskiverrosti ja huonosti pärjääviin. Mallia sovelletaan onnistuneesti myös tunnistamaan käytetty ohjelmointikieli käyttämällä vain tilakoneen tilasiirtymiä

Measuring Outcome Expectations in Academic Persistence

Academic persistence, or a student’s decision to leave an institution of higher education, has remained an inveterate puzzle to researchers, theoreticians, institutions, and counselors. Despite a large body of theoretical and empirical literature, the rate at which students leave institutions of higher education has remained stable over the past 50 years. The discipline of counseling psychology has a long tradition of investigating academic persistence from a psychological perspective. Earlier investigations in counseling psychology focused on identifying psychopathological traits, cognitive abilities, and contextual factors associated with a student’s decision to leave. These investigations were met with a sociological reaction that has dominated the question of persistence for the past forty years. Though useful in describing the institution’s role in persistence, these models lack substantial empirical support and are fraught with conceptual problems. Meta-analytic studies investigating non-cognitive factors in academic persistence have revealed that social cognitive constructs namely academic self-efficacy and goals are predictive of student retention when traditional predictors are accounted for (Robbins et al., 2004). However, outcome expectations, an integral theoretical component of social cognitive theory, remain almost completely unexamined in the domain of academic persistence. This study sought to develop a theoretically derived scale to measure outcome expectations in the domain of academic persistence. An initial item pool was developed and sent to a sample of college students (N = 216). A second, confirmatory sample of undergraduate students was collected via an online crowdsourcing format known as Prolific Academic (N = 301). Results suggested the presence of a two-factor structure was the most parsimonious solution that fit the data rather than the hypothesized three-factor structure. The two factors retained across both samples anticipated rewards and punishments that students perceived about remaining in college for the year. This was contrary to Bandura’s (1977, 1997) hypothesis that outcome expectations conformed to three classes. Limitations and implications are discussed

An analysis of students’ behaviour in a Learning Management System through Process Mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe exponential growth and transformation of the Internet and information technology in recent years led to the development of several analytical tools. As is the case with process mining, it emerged to fulfill the need to extract and analyze information from event logs by representing it in the form of process models. Process mining is an acclaimed tool and proved crucial in several areas, from healthcare to manufacturing and finance. Nevertheless, and despite the crucial role of digital systems in supporting learning activities and generating large amounts of data about learning processes, limited research focused on process mining applied to the educational context. Therefore, the aim of this dissertation is to apply a process-oriented approach and demonstrate the applicability of process mining techniques to explore and analyze students’ behavior and interaction patterns, based on data collected from Moodle, the widely used Learning Management System. We cover definitions of process mining, education, and a detailed search of the existing literature on educational process mining during this work. Furthermore, the paper analyzes and discusses the findings of the study that combines process mining techniques, specifically process discovery implanted in the Disco tool, with cluster analysis. Through the application of these two techniques, it was possible to recognize the relationship between the students’ behavior registered in the process models and the success of the students in the course, along with the general and specific information about the students’ learning paths. Besides, we obtained findings that allow us to predict the group of students at risk of failing. Finally, with the analysis of these results, we were able to provide improvement proposals and recommendations to enhance the learning experience