Dropout Predictions of Ideological and Political MOOC Learners Based on Big Data

The massive open online course (MOOC) has expanded rapidly, providing users with a low-cost, high-quality learning experience. High dropout rate is a serious obstacle that restricts the development of ideological and political MOOC. One of the ways to solve this obstacle is to use the rich data resources in MOOC to explore the relevant factors of dropout. Reduce dropout rates by building drop-out prediction models and establishing early-warning mechanisms. However, the ideological MOOC data is huge and complex, which is prone to problems such as loss of data value, mismatch between data and models, and poor research reproducibility. This paper uses a more mature logistic regression method of machine learning to transfer it to the field of education, providing a new path for data-driven MOOC dropout prediction research

EU–originated MOOCs, with focus on multi- and single-institution platforms

No abstract available

The efficacy of the “Talk-to-Me” suicide prevention and mental health education program for tertiary students: a crossover randomised control trial

Despite suicide ideation being one of the most frequently reported health issues impacting tertiary students, there is a paucity of research evaluating the efficacy of preventive interventions aimed at improving mental health outcomes for students studying at two tertiary institutes. The current study evaluated the efficacy of the “Talk-to-Me” Mass Open Online Course (MOOC) in improving tertiary students’ abilities to support the mental health of themselves and their peers via a randomised controlled trial design, comparing them to a waitlist control group. Overall, 129 tertiary students (M = 25.22 years, SD = 7.43; 80% female) undertaking a health science or education course at two Western Australian universities were randomly allocated to either “Talk-to-Me” (n = 66) or waitlist control (n = 63) groups. The participants’ responses to suicidal statements (primary outcome), knowledge of mental health, generalised self-efficacy, coping skills, and overall utility of the program (secondary outcomes) were collected at three timepoints (baseline 10-weeks and 24-weeks from baseline). Assessment time and group interaction were explored using a random-effects regression model, examining changes in the primary and secondary outcomes. Intention-to-treat analysis (N = 129) at 10-weeks demonstrated a significant improvement in generalised self-efficacy for “Talk-to-Me” compared to the control group (ES = 0.36, p = .04), with only the “Talk-to-Me” participants reporting increased knowledge in responding to suicidal ideation (primary outcome). This change was sustained for 24 weeks. Findings provide preliminary evidence suggesting that the “Talk-to-Me” MOOC can effectively improve tertiary students’ mental health and knowledge of how to support themselves and others in distress. ACTRN12619000630112, registered 18-03-2019, anzctr.org.au. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00787-022-02094-4

Student data prediction

One of the great challenges for education is to be able to offer distance learning programs where students feel that these programs are an added value to their academic training. Although technological development makes it possible to overcome the physical barriers of a classroom and thus reach many more students with this distance learning offer, there are at the same time a greater number of difficulties in reaching them with the proper quality. The lack of selection of candidates, the suitability of the programs for distance learning or the lack of interaction between students and between students and teachers are factors that contribute to high dropout rates of students in these distance learning courses. The purpose of this dissertation is to figure out which factors contribute the most to dropout rates, how we can identify in advance students who are at risk of dropping out, and how we can act to decrease this risk. To do this, we will use data from distance programs of soft skills where an exploratory analysis of the data will be done to understand which factors contribute most to this dropout rate and where machine learning algorithms will be applied to classify the students at risk of dropping out, thus being possible to identify these students in advance and promote actions to avoid these dropouts.Um dos grandes desafios para a educação é de ser capaz de oferecer programas de ensino à distância onde os estudantes possam sentir que são uma mais-valia para a sua formação académica. Embora o desenvolvimento tecnológico torne possível ultrapassar as barreiras físicas de uma sala de aula e desta forma alcançar muito mais estudantes, há, ao mesmo tempo, uma maior dificuldade em fazer com que a oferta tenha a qualidade espectável. A falta de seleção de candidatos, a adequação dos programas ao ensino à distância ou a falta de interação entre estudantes e entre estudantes e docentes são fatores que contribuem para taxas de abandono escolar nestes cursos de ensino à distância. O objetivo deste documento é tentar compreender quais os fatores que mais contribuem para as taxas de abandono escolar, como identificar antecipadamente os alunos em risco de abandono escolar, e como agir de forma a diminuir este risco. Para tal, utilizaremos dados de programas à distância de soft skills. É feita uma análise exploratória dos dados para compreender quais os fatores que mais contribuem para esta taxa de abandono escolar, são aplicados algoritmos de aprendizagem automática para classificar os estudantes em risco de abandono escolar, sendo assim possível identificar estes estudantes com antecedência e promover ações para evitar estas desistências

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

With the widespread of Small Private Online Courses (SPOC) in colleges and universities, course organizers who provide high-quality personalized course activities need to understand learners' learning status and characteristics, and then optimize the course teaching. However, little research has been done on different learners' group behavior characteristics, such as which indicators can reflect learner groups' behavior, and what are the typical behavior characteristics of different learner groups. In this work, we established a Python Language Foundation self-built SPOC course, and 109 undergraduates' learning behavior data were collected and analyzed. From 74-dimensional behavior log data consisting of 72 video-viewing, course video score, and comprehensive score, Principal Component Analysis was performed to reduce dimensionality. Agglomerative hierarchical clustering was used to divide learners into different categories, and the results showed that 15 groups are clustered. According to the analysis of the four indicators for group characteristics, which are the completion and viewing-stability of task-point videos, unit test excellence, and comprehensive score, it is identified into five primary types, including positive type, regular type, negative type, semi-negative type, and a fluctuation type. Experiments conducted on a real data set across different academic years and courses show that the proposed approach has better clustering accuracy and practicability. It is expected that this work may be used to strengthen the personalized learning support services system in educational institutions and develop a tool that integrates exploration and analysis work onto the web platform