Recognizing Students At-Danger With Early Intervention Using Machine Learning Techniques

Students in online courses require attention as there is no much interaction between the teacher and student compared to traditional instructing methods. Due to the increase in advent of massive open online courses, there is a need to focus on identifying students at danger of withdrawl or failure. As the count of students enrolling in an online course is huge it’s quite difficult to find out specific students who are at-danger of failure/withdrawal from the course. There is a need to alleviate this problem by identifying those students and help academic instructors offer support to them. The major contribution of this work is to analyze the risk associated with the dropout of student in order to aid instructors in delivering the intensive intervention support to student who is at verge of quitting from the course. The main objective is to track student performance and provide valuable information to the educator to subsequent the courses according to their learning achievement and also help academic advisors to detect the student having low academic achievement records and encourage the candidates.  Data collected from OULAD datset is analyzed with the help at -risk prediction model is to identify whether a student is at verge of withdrawal or not

Investigating Variation in Learning Processes in a FutureLearn MOOC

Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining (EPM) in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals

Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques

Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely at-risk student model and learning achievement model. The models have the potential to detect the students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in at-risk student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at-risk students

Mineração de dados educacionais : um estudo de caso nos departamentos de Engenharia Elétrica e Ciência da Computação

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2019.A crescente preocupação com a eficiência do ensino, principalmente no nível superior, tem motivado a busca por formas de se compreender e aplicar melhor conceitos pedagógicos e fenômenos educacionais. Com isso, diversas áreas começaram a colaborar na busca de soluções para o processo educacional. Nesse contexto, a solidificação da área Educational Data Mining ao longo da década dos anos 2000 se mostra como uma ferramenta poderosa. Neste trabalho, são apresentadas métricas estatísticas que permitem analisar informações sobre os estudantes do curso de Engenharia da Computação, sobre as disciplinas cursadas por esses estudantes e todas as disciplinas ofertadas pelos departamentos de Engenharia Elétrica e Ciência da Computação da Universidade de Brasília. Essas métricas buscam correlacionar informações, a fim de descobrir relações mais complexas e assim possibilitar a tomada de decisões em diversos âmbitos com base neste estudo. Além disso, foi desenvolvido um modelo preditivo da desistência dos estudantes que, de forma automática, estima a probabilidade de um determinado estudante concluir ou não a sua graduação. O modelo preditivo desenvolvido, baseado em árvores de decisão, busca não só oferecer uma forma de se identificar estudantes com altas chances de sair do curso, mas também prover in formações qualitativas sobre a correlação do desempenho dos estudantes ao longo dos semestres analisados e a sua desistência.The rising concern about efficiency in education, especially regarding higher education, has sti mulated searches for methods to better comprehend and apply pedagogical concepts and other phenomena related to the educational field. Accordingly, many academical fields began colla borating in order to reach solutions for the educational process. Thus, the consolidation of the Educational Data Mining field along the decade of the 2000s has presented itself as a powerful tool. In this paper, statistical metrics are presented which facilitate the analysis of information regarding students of the computer engineering course, with respect to subjects these students participated and all other disciplines which are offered by both the Electric Engineering and the Computer Science departments of the Universidade de Brasilia. These metrics attempt to correlate information, in order to find out complex relations so that it is possible to make decisions in many ways that are influenced by this study. Also, a model was developed to predict student’s dropping out, so in an automatic way, it works to figure out if a certain student would graduate or if he would drop out. The predictive model created, is based on decision trees, not only intends to conceive a way to identify students with a high potential to drop out but it also provides qualitative information regarding the correlation between students’ performance along the semesters analyzed and they effectively dropping out

Learning Analytics Through Machine Learning and Natural Language Processing

The increase of computing power and the ability to log students’ data with the help of the computer-assisted learning systems has led to an increased interest in developing and applying computer science techniques for analyzing learning data. To understand and investigate how learning-generated data can be used to improve student success, data mining techniques have been applied to several educational tasks. This dissertation investigates three important tasks in various domains of educational data mining: learners’ behavior analysis, essay structure analysis and feedback providing, and learners’ dropout prediction. The first project applied latent semantic analysis and machine learning approaches to investigate how MOOC learners’ longitudinal trajectory of meaningful forum participation facilitated learner performance. The findings have implications on refining the courses’ facilitation methods and forum design, helping improve learners’ performance, and assessing learners’ academic performance in MOOCs. The second project aims to analyze the organizational structures used in previous ACT test essays and provide an argumentative structure feedback tool driven by deep learning language models to better support the current automatic essay scoring systems and classroom settings. The third project applied MOOC learners’ forum participation states to predict dropouts with the help of hidden Markov models and other machine learning techniques. The results of this project show that forum behavior can be applied to predict dropout and evaluate the learners’ status. Overall, the results of this dissertation expand current research and shed light on how computer science techniques could further improve students’ learning experience