Agent-Based Models (ABMs) have been used in the field of education as a learning tool. However, the use of ABM as a tool for educational stakeholders is not well-represented, nor machine learning (ML) for ABM. Here we extend our work on a classroom ABM, by developing an ABM & ML hybrid model that simulates classroom disruptive interactions during a school year, and outputs predicted learning outcomes. We use real-life data from a primary school monitoring system that contains 65,385 student records from 2,040 schools across the UK as well as simulated interaction data, to implement linear regression for predictions of math scores, and ABM interactions to update the final score, to reflect the effect of these interactions. We show that this hybrid ABM model outperforms a simple ABM model.

Discussion forums on MOOCs are developing as a major tool for communication between learners and instructors, generating large amounts of posts, exchanged as unstructured text content. Thus, it is a major challenge for instructors to find (and respond to) urgent posts, amongst the vast amount of posts. Learners also may inadvertently pose posts that may seem more urgent than they are. However, the current literature lacks research on analysing posts from an urgency language perspective, i.e., extracting the language used for urgent expression . This paper explores for the first time the urgent language that learners use to express their need for immediate intervention, via an automatised approach. It describes our analysis of 5181 text posts from a course from the Stanford MOOCPosts dataset, selected for its good representation of urgent posts. We use topic modelling, here, via the widely used latent Dirichlet allocation (LDA). Moreover, we demonstrate a correlation between specific topics and urgent posts. Also, we show that most urgent posts start new threads. Additionally, constructing a visual interface for instructors or learners may support them understand the urgent language and improve intervention.

The classroom environment is a major contributor to the learning process in schools. Young students are affected by different details in their academic progress, be it their own characteristics, their teacher’s or their peers’. The combination of these factors is known to have an impact on the attainment of young students. However, what is less known are ways to accurately measure the impact of the individual variables. Moreover, in education, predicting an end-result is not enough, but understanding the process is vital. Thus, in this paper, we simulate the interactions between these factors to offer education stakeholders – administrators and teachers, in a first instance – the possibility of understanding how their activities and the way they manage the classroom can impact on students’ academic achievement and result in different learning outcomes. The simulation is based on data from Performance Indicator in Primary Schools (PIPS) monitoring system, of 65,385 records that include 3,315 classes from 2,040 schools, with an average of 26 students per class collected in 2007. The results might serve teachers in solving issues that occur in classrooms and improve their strategies based on the predicted outcome.

Schoolchildren's academic progress is known to be affected by the classroom environment. It is important for teachers and administrators to understand their pupils' status and how various factors in the classroom may affect them, as it can help them adjust pedagogical interventions and management styles. In this study, we expand a novel agent-based model of classroom interactions of our design, towards a more efficient model, enriched with further parameters of peers and teacher's characteristics, which we believe renders a more realistic setting. Specifically, we explore the effect of disruptive neighbours and teacher control. The dataset used for the design of our model consists of 65,385 records, which represent 3,315 classes in 2007, from 2,040 schools in the UK.

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil, a limited number of pupils leaving high schools continue their education (up to 20%). Initial pioneering efforts of universities and companies to support pupils from underprivileged backgrounds, to be able to succeed in being accepted by universities include personalised learning solutions. However, initial findings show that typical distance learning problems occur with the pupil population: isolation, demotivation, and lack of engagement. Thus, researchers and companies proposed gamification. However, gamification design is traditionally exclusively based on theory-driven approaches and usually ignore the data itself. This paper takes a different approach, presenting a large-scale study that analysed, statistically and via machine learning (deep and shallow), the first batch of students trained with a Brazilian gamified intelligent learning software (called CamaleOn), to establish, via a grassroots method based on learning analytics, how gamification elements impact on student engagement. The exercise results in a novel proposal for realtime measurement on Massive Open Online Courses (MOOCs), potentially leading to iterative improvements of student support. It also specifically analyses the engagement patterns of an underserved community.

In recent years, massive open online courses (MOOCs) have become one of the most exciting innovations in e-learning environments. Thousands of learners around the world enroll on these online platforms to satisfy their learning needs (mostly) free of charge. However, despite the advantages MOOCs offer learners, dropout rates are high. Struggling learners often describe their feelings of confusion and need for help via forum posts. However, the often-huge numbers of posts on forums make it unlikely that instructors can respond to all learners and many of these urgent posts are overlooked or discarded. To overcome this, mining raw data for learners' posts may provide a helpful way of classifying posts where learners require urgent intervention from instructors, to help learners and reduce the current high dropout rates. In this paper we propose, a method based on correlations of different dimensions of learners' posts to determine the need for urgent intervention. Our initial statistical analysis found some interesting significant correlations between posts expressing sentiment, confusion, opinion, questions, and answers and the need for urgent intervention. Thus, we have developed a multidimensional deep learner model combining these features with natural language processing (NLP). To illustrate our method, we used a benchmark dataset of 29598 posts, from three different academic subject areas. The findings highlight that the combined, multi-dimensional features model is more effective than the text-only (NLP) analysis, showing that future models need to be optimised based on all these dimensions, when classifying urgent posts.