Enjoyed or bored?:A study into achievement emotions and the association with barriers to learning in MOOCs

MOOCs are accessible online personal development opportunities in which learners can expand their knowledge on many topics. Yet, the experience of barriers to learning often hinders learners from achieving their personal learning goals. Therefore, it is important to have insight into determinants that may influence the experience of (certain) barriers. This study investigated whether the emotional determinants enjoyment and boredom, which are known to impact learner achievement and motivation, affect the experience of (specific) barriers while learning in MOOCs. The results show that boredom did affect the experience of barriers related to technical and online related skills, social context and time, support and motivation, yet it did not affect the experience of barriers related to the design of the MOOC. Enjoyment was not correlated to any of the barriers. Furthermore, the same analysis comparing men to women again revealed that boredom did not significantly affect the experience of barriers related to the design of the MOOC, yet did significantly affect the experience of the other barriers. No, significant differences were found between males and females. These findings may serve as input for supporting learners in achieving their individual learning goals

Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling

ITS authoring tools make creating intelligent tutoring systems more cost effective, but few authoring tools make it easy to flexibly incorporate an open-ended range of student modeling methods and learning analytics tools. To support a cumulative science of student modeling and enhance the impact of real-world tutoring systems, it is critical to extend ITS authoring tools so they easily accommodate novel student modeling methods. We report on extensions to the CTAT/Tutorshop architecture to support a plug-in approach to extensible student modeling, which gives an author full control over the content of the student model. The extensions enhance the range of adaptive tutoring behaviors that can be authored and support building external, student- or teacher-facing real-time analytics tools. The contributions of this work are: (1) an open architecture to support the plugging in, sharing, re-mixing, and use of advanced student modeling techniques, ITSs, and dashboards; and (2) case studies illustrating diverse ways authors have used the architecture

Biosignals reflect pair-dynamics in collaborative work : EDA and ECG study of pair-programming in a classroom environment

Collaboration is a complex phenomenon, where intersubjective dynamics can greatly affect the productive outcome. Evaluation of collaboration is thus of great interest, and can potentially help achieve better outcomes and performance. However, quantitative measurement of collaboration is difficult, because much of the interaction occurs in the intersubjective space between collaborators. Manual observation and/or self-reports are subjective, laborious, and have a poor temporal resolution. The problem is compounded in natural settings where task-activity and response-compliance cannot be controlled. Physiological signals provide an objective mean to quantify intersubjective rapport (as synchrony), but require novel methods to support broad deployment outside the lab. We studied 28 student dyads during a self-directed classroom pair-programming exercise. Sympathetic and parasympathetic nervous system activation was measured during task performance using electrodermal activity and electrocardiography. Results suggest that (a) we can isolate cognitive processes (mental workload) from confounding environmental effects, and (b) electrodermal signals show role-specific but correlated affective response profiles. We demonstrate the potential for social physiological compliance to quantify pair-work in natural settings, with no experimental manipulation of participants required. Our objective approach has a high temporal resolution, is scalable, non-intrusive, and robust.Peer reviewe

Building a semantic ontology for virtual peers in narrative-based environments

Narrative-based environments utilize various forms of knowledge to provide an interactive space for the learner and the virtual agent to collaborate in accomplishing the learning goals. In this paper, we present the design of a semantic ontology that provides the necessary domain-based conceptual knowledge to allow a virtual peer to engage in storytelling as a form of exchange with the learner. We then show how the ontology was utilized to support the virtual peer in performing its tasks, which include generating interactive stories that teach about appropriate social behavior, and engaging in a text-based dialogue with the learner. © Springer International Publishing AG 2017

A Study of Automatic Speech Recognition in Noisy Classroom Environments for Automated Dialog Analysis

The development of large-scale automatic classroom dialog analysis systems requires accurate speech-to-text translation. A variety of automatic speech recognition (ASR) engines were evaluated for this purpose. Recordings of teachers in noisy classrooms were used for testing. In comparing ASR results, Google Speech and Bing Speech were more accurate with word accuracy scores of 0.56 for Google and 0.52 for Bing compared to 0.41 for AT&T Watson, 0.08 for Microsoft, 0.14 for Sphinx with the HUB4 model, and 0.00 for Sphinx with the WSJ model. Further analysis revealed both Google and Bing engines were largely unaffected by speakers, speech class sessions, and speech characteristics. Bing results were validated across speakers in a laboratory study, and a method of improving Bing results is presented. Results provide a useful understanding of the capabilities of contemporary ASR engines in noisy classroom environments. Results also highlight a list of issues to be aware of when selecting an ASR engine for difficult speech recognition tasks