Identification of Affective States in MOOCs: A Systematic Literature Review

Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction,  no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent

A review on data fusion in multimodal learning analytics and educational data mining

The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area

Student Perceptions and Engagement in Video-based Learning for Microbiology Education

Online learning increases the physical distance between instructors and students and depending on the mode of delivery, it can be challenging to close this gap. To ameliorate this potential for student isolation, instructors need to communicate to students in a variety of ways, blending original online resources with synchronous interactive learning activities. During 2020, 34 lecture videos were created for a large undergraduate microbiology and immunology course offered at The University of Queensland. The teaching team applied a subset of Mayer’s multimedia learning design principles – embodiment, mixed perspectives, segmenting, signalling – to create videos featuring instructor presence, multiple presentation styles, and dynamic pacing. When compared to voice-over presentations created by automated lecture capture software, the outcomes of this design process increased student engagement in video-based learning across the 2020 and 2021 course offerings. Analysis of student perception data collected by online questionnaires and interviews revealed broad agreement with the design principles used for video-based learning. However, their value of on-screen instructor visibility, graphics, and text was variable as a result of individual preferences. Together these findings present a case study in which instructional videos were developed iteratively through the selective application of multimedia design principles and strategic adaptation of existing learning resources

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

Designing Attentive Information Dashboards

Information dashboards are a critical capability in contemporary business intelligence and analytics systems. Despite their strong potential to support better decision-making, the massive amount of information they provide challenges users performing data exploration tasks. Accordingly, dashboard users face difficulties in managing their limited attentional resources when processing the presented information on dashboards. Also, studies have shown that the amount of concentrated time humans can spend on a task has dramatically decreased in recent years; thus, there is a need for designing user interfaces that support users attention management. In this design science research project, we propose attentive information dashboards that provide individualized visual attention feedback (VAF) as an innovative artifact to solve this problem. We articulate theoretically grounded design principles and instantiate a software artifact leveraging users eye movement data in real time to provide individualized VAF. We evaluated the instantiated artifact in a controlled lab experiment with 92 participants. The results from analyzing users eye movement after receiving individualized VAF reveal that our proposed design has a positive effect on users attentional resource allocation, attention shift rate, and attentional resource management. We contribute a system architecture for attentive information dashboards that support data exploration and two theoretically grounded design principles that provide prescriptive knowledge on how to provide individualized VAF. Practitioners can leverage the prescriptive knowledge derived from our research to design innovative systems that support users information processing by managing their limited attentional resources

Improving Mobile MOOC Learning via Implicit Physiological Signal Sensing

Massive Open Online Courses (MOOCs) are becoming a promising solution for delivering high- quality education on a large scale at low cost in recent years. Despite the great potential, today’s MOOCs also suffer from challenges such as low student engagement, lack of personalization, and most importantly, lack of direct, immediate feedback channels from students to instructors. This dissertation explores the use of physiological signals implicitly collected via a "sensorless" approach as a rich feedback channel to understand, model, and improve learning in mobile MOOC contexts. I first demonstrate AttentiveLearner, a mobile MOOC system which captures learners' physiological signals implicitly during learning on unmodified mobile phones. AttentiveLearner uses on-lens finger gestures for video control and monitors learners’ photoplethysmography (PPG) signals based on the fingertip transparency change captured by the back camera. Through series of usability studies and follow-up analyses, I show that the tangible video control interface of AttentiveLearner is intuitive to use and easy to operate, and the PPG signals implicitly captured by AttentiveLearner can be used to infer both learners’ cognitive states (boredom and confusion levels) and divided attention (multitasking and external auditory distractions). Building on top of AttentiveLearner, I design, implement, and evaluate a novel intervention technology, Context and Cognitive State triggered Feed-Forward (C2F2), which infers and responds to learners’ boredom and disengagement events in real time via a combination of PPG-based cognitive state inference and learning topic importance monitoring. C2F2 proactively reminds a student of important upcoming content (feed-forward interventions) when disengagement is detected. A 48-participant user study shows that C2F2 on average improves learning gains by 20.2% compared with a non-interactive baseline system and is especially effective for bottom performers (improving their learning gains by 41.6%). Finally, to gain a holistic understanding of the dynamics of MOOC learning, I investigate the temporal dynamics of affective states of MOOC learners in a 22 participant study. Through both a quantitative analysis of the temporal transitions of affective states and a qualitative analysis of subjective feedback, I investigate differences between mobile MOOC learning and complex learning activities in terms of affect dynamics, and discuss pedagogical implications in detail

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Influence of employer support for professional development on MOOCs enrolment and completion: Results from a cross-course survey

Although the potential of open education and MOOCs for professional development is usually recognized, it has not yet been explored extensively. How far employers support non-formal learning is still an open question. This paper presents the findings of a survey-based study which focuses on the influence of employer support for (general) professional development on employees’ use of MOOCs. Findings show that employers are usually unaware that their employees are participating in MOOCs. In addition, employer support for general professional development is positively associated with employees completing MOOCs and obtaining certificates for them. However, the relationship between employer support and MOOC enrollment is less clear: workers who have more support from their employers tend to enroll in either a low or a high number of MOOCs. Finally, the promotion of a minimum of ICT skills by employers is shown to be an effective way of encouraging employee participation in the open education ecosystem.JRC.J.3-Information Societ