Mature cystic teratoma of mediastinum compressing the right atrium in a child: A rare case report

Teratomas or Germ cell tumours (GCTs) are interesting because of their obscure origin, bizarre microscopic appearance and unpredictable behaviour. Mediastinal teratoma is a slowly growing and rare tumour found in children that is diagnosed incidentally in asymptomatic patients. Most of the symptoms are related to mass compression effects such as chest pain, cough, respiratory distress and dysphagia. We report a 5-year-old male child who presented with a history of foreign body ingestion, sternal and left upper quadrant pain and vomiting without respiratory distress. The patient was incidentally found to have an anterior mediastinal mass compressing the right atrium and was diagnosed by histopathological examination as having a mature cystic teratoma of the mediastinum. The patient was successfully treated by the surgical resection of the tumour. Keywords: Anterior mediastinum, Children, Histopathology, Surgical treatment, Teratom

A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts

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

Agent-based simulation of the classroom environment to gauge the effect of inattentive or disruptive students

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

Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs

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 real-time 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