Mouse dynamics correlates to student behaviour in computer-based exams

Nowadays, it is common for higher education institutions to use computer-based exams, partly or integrally, in their evaluation processes. These exams, much like their paper-based counterparts, are one of the most significant sources of stress in the life of students. However, the fact that exams are undertaken in a computer allows for new features to be acquired that may provide more reliable insights into the behaviour and state of the student during the exam. In this article we analyse these novel behavioural features and explore, to which extent, they can point out previously unknown phenomena. Specifically, we show that the time a student takes to complete an exam is correlated with mouse dynamics features. In practical terms, we are able to predict the duration of each individual exam with a satisfying error based on the interaction patterns of the student.COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. This work was funded by ‘EUSTRESS – Sistema de Informação para a monitorização e avaliação ̧ dos níveis do stress e previsão de stress cónico’, N◦2015/017832 P2020 SI I&DT, (NUP, NORTE-01-0247-FEDER-017832) in co-promotion between Optimizer-Lda and ICVS/3B’s-Uminhoinfo:eu-repo/semantics/publishedVersio

EUStress: A human behaviour analysis system for monitoring and assessing stress during exams

In today’s society, there is a compelling need for innovative approaches for the solution of many pressing problems, such as understanding the fluctuations in the performance of an individual when involved in complex and high-stake tasks. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. Human stress can be viewed as an agent, circumstance, situation, or variable that disturbs the normal functioning of an individual, that when not managed can bring mental problems, such as chronic stress or depression. In this paper, we propose a different approach for this problem. The EUStress application is a non-intrusive and non-invasive performance monitoring environment based on behavioural biometrics and real time analysis, used to quantify the level of stress of individuals during online exams.FCT - Fuel Cell Technologies Program(NORTE-01-0247-FEDER-017832)info:eu-repo/semantics/publishedVersio

Objectively measured sedentary time and academic achievement in schoolchildren

Published online: 26 Apr 2016. [Epub ahead of print]This study aimed to evaluate the relationship between objectively measured total sedentary time and academic achievement (AA) in Portuguese children. The sample comprised of 213 children (51.6% girls) aged 9.46 ± 0.43 years, from the north of Portugal. Sedentary time was measured with accelerometry, and AA was assessed using the Portuguese Language and Mathematics National Exams results. Multilevel linear regression models were fitted to assess regression coefficients predicting AA. The results showed that objectively measured total sedentary time was not associated with AA, after adjusting for potential confounders.Portuguese Foundation for Science and Technology - BPD/102381/2014 and UID/DTP/00617/2013CIEC – Research Centre on Child Studies, IE, UMinho (FCT R&D unit 317), PortugalAustralian Research Council (DE150101921

Seeking Flow from Fine-Grained Log Data

Flow is the experience of deep absorption in a demanding, intrinsically-motivating task conducted with skill. We consider how to measure behavioural correlates of flow from fine-grained process data extracted from programming environments. Specifically, we propose measuring affective factors related to flow non-intrusively based on log data. Presently, such affective factors are typically measured intrusively (by self-report), which naturally will break the flow. We evaluate our approach in a pilot study, where we use log data and survey data collected from an introductory programming course. The log data is fine-grained, containing timestamped actions at the keystroke level from the process of solving programming assignments, while the survey data has been collected at the end of every completed assignment. The survey data in the pilot study comprises of Likert-like items measuring perceived educational value, perceived difficulty, and students' self-reported focus when solving the assignments. We study raw and derived log data metrics, by looking for relationships between the metrics and the survey data. We discuss the results of the pilot study and provide suggestions for future work related to non-intrusive measures of programmer affect.Peer reviewe

Proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET 2013)

"This book contains the proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET) 2013 which was held on 16.-17.September 2013 in Paphos (Cyprus) in conjunction with the EC-TEL conference. The workshop and hence the proceedings are divided in two parts: on Day 1 the EuroPLOT project and its results are introduced, with papers about the specific case studies and their evaluation. On Day 2, peer-reviewed papers are presented which address specific topics and issues going beyond the EuroPLOT scope. This workshop is one of the deliverables (D 2.6) of the EuroPLOT project, which has been funded from November 2010 – October 2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission through the Lifelong Learning Programme (LLL) by grant #511633. The purpose of this project was to develop and evaluate Persuasive Learning Objects and Technologies (PLOTS), based on ideas of BJ Fogg. The purpose of this workshop is to summarize the findings obtained during this project and disseminate them to an interested audience. Furthermore, it shall foster discussions about the future of persuasive technology and design in the context of learning, education and teaching. The international community working in this area of research is relatively small. Nevertheless, we have received a number of high-quality submissions which went through a peer-review process before being selected for presentation and publication. We hope that the information found in this book is useful to the reader and that more interest in this novel approach of persuasive design for teaching/education/learning is stimulated. We are very grateful to the organisers of EC-TEL 2013 for allowing to host IWEPLET 2013 within their organisational facilities which helped us a lot in preparing this event. I am also very grateful to everyone in the EuroPLOT team for collaborating so effectively in these three years towards creating excellent outputs, and for being such a nice group with a very positive spirit also beyond work. And finally I would like to thank the EACEA for providing the financial resources for the EuroPLOT project and for being very helpful when needed. This funding made it possible to organise the IWEPLET workshop without charging a fee from the participants.

Higher education leadership and communication apprehension reduction efficacy

Universities use public speaking courses to reduce the communication apprehension among the student population of the institution. Previous research connects lower communication apprehension to higher university retention rate and higher student success rates. However, a gap in current research does not explain if communication apprehension reduction is as significant when a student enrolls in a public speaking course taught online as opposed to a public speaking course taught in a traditional face-to-face manner. The purpose of this study was to explore the efficacy of systematic desensitization as it is used in public speaking courses online. This study set-out to answer two research questions: 1. Does the technique of systematic desensitization significantly lower communication apprehension for students taking a public speaking course online compared to traditionally taught face-to-face courses? 2. Do students who choose online public speaking courses have a higher level of communication apprehension than those who choose the traditionally taught public speaking courses? This study found that there were no significant differences in lowering communication apprehension when comparing effects of systematic desensitization methods from the public speaking courses taught online and those taught traditionally. The study also found that there was no significant difference in levels of communication apprehension for student populations which chose to enroll in the online public speakingcourse when compared to the traditionally taught course. The benefits of offering public speaking to lower communication apprehension for students in a higher education setting are seen similarly in the online offerings as they are seen in the traditional courses

Two Roads Diverge: Mapping the Path of Learning for Novice Programmers Through Large Scale Interaction Data and Neural Network Classifiers

Learning to program is a fundamental part of Computer Science education. To become a proficient programmer, one must become competent at both code comprehension and code production. Research shows that the most effective way to teach programming to students is through practical exercises. However, the increasing numbers of students in Computer Science classes means it is difficult to correct assignments and provide timely feedback. This can result in fewer practical assignments and/or less useful feedback for each student. Automated grading tools, and understanding of how novice programmers learn to code, is essential for these growing numbers of students. The Maynooth University Learning Environment, or MULE, was built to address this challenge. MULE is a cloud-based learning environment built from the ground up with the goal of teaching introductory programming courses in an authentic manner while facilitating the collection of large-scale behavioural data to support Learning Analytics. In this thesis, behavioural interaction data and code written by students in MULE is used to investigate the differences between successful and unsuccessful programming student behaviour, with the use of data analysis and Neural Network classifiers. The result is a method of classification that predicts early on if a student is likely to be in the top or bottom 50% of grades in the class with up to 87% accuracy, and a model of the path of learning for successful students, including key times, assignments, and topics during the introduction to programming module when the higher and lower achieving students diverge in behaviour