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How can Interaction Data be Contextualized with Mobile Sensing to Enhance Learning Engagement Assessment in Distance Learning?
... A potential improvement could be context-awareness of learning activities (Ciordas-Hertel et al., 2022). Given that learning materials are increasingly available on online learning platforms, a learner's activity could also serve as a contextual trigger to activate restrictions. ...
Distractions are ubiquitous in today’s technology-saturated environments, an issue that significantly impacts learning contexts employing digital technologies and yields detrimental effects on learning. Digital self-control tools, which aim to assist users in their efforts to reduce digital distractions, are numerous and readily available. Despite several dedicated empirical studies focusing on specific tools, there remains a notable lack of information regarding their daily use and helpfulness. Furthermore, the sheer variety of these tools prompts questions about their universal helpfulness and the potential influence of individual differences.
To address these issues, we surveyed a sample of higher-education students, totaling 273 individuals. These students reported on their media use, satisfaction with learning, and experiences with features of digital self-control tools. Our study’s findings indicate a discrepancy in the perception and awareness of these features; those deemed most helpful are among the least known, and conversely, common features are often perceived as unhelpful.
Our research also uncovered a negative correlation between habitual media use and the use of less restrictive features. Another identified issue was constraints on the use of these tools for learning, as platforms often serve dual purposes for both education and entertainment. We delve into these practical problems and propose future research directions to further advance the understanding of digital self-control tools.
Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. In this paper, we review the state-of-the-art approaches for defining a user learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations.
Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations.
Framed by social learning theory, the study examines a set of personal and social factors determining off-task social media multitasking inside university classrooms. We aim to clarify the relationships between social media multitasking and self-efficacy, intrinsic motivation, multitasking preference as well as peer distraction, and to elucidate the interactive relationships between these factors. Questionnaire data from 203 university students in China show that academic self-efficacy fully mediates the association between intrinsic motivation and off-task multitasking. Moreover, multitasking preference partially mediates the association between peer distraction and off-task multitasking during class time. The findings of the study contribute to a deeper understanding of why students multitask during class, which can inform the development of strategies for combating social media distraction and enhancing students’ learning engagement.
Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12 commitments that we believe are critical for creating effective MMLA innovations. Moreover, as MMLA grows in use, it is important to articulate a set of core commitments that can help guide both MMLA researchers and the broader learning analytics community. The commitments that we describe are deeply rooted in the origins of MMLA and also reflect the ways that MMLA has evolved over the past 10 years. We organize the 12 commitments in terms of (i) data collection, (ii) analysis and inference, and (iii) feedback and data dissemination and argue why these commitments are important for conducting ethical, high-quality MMLA research. Furthermore, in using the language of commitments, we emphasize opportunities for MMLA research to align with established qualitative research methodologies and important concerns from critical studies.
Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.
Nowadays, many people take short breaks with their smartphone at work. The decision whether to continue working or to take a smartphone break is a so-called labour versus leisure decision. Motivational models predict that people are more likely to switch from labour (work) to leisure (smartphone) the more fatigue or boredom they experience. In turn, fatigue and boredom are expected to decrease after the smartphone was used. However, it is not yet clear how smartphone use at work relates to fatigue and boredom. In this study, we tested these relationships in both directions. Participants ( n = 83, all PhD candidates) reported their current level of fatigue and boredom every hour at work while an application continuously logged their smartphone use. Results indicate that participants were more likely to interact with their smartphone the more fatigued or bored they were, but that they did not use it for longer when more fatigued or bored. Surprisingly, participants reported increased fatigue and boredom after having used the smartphone (more). While future research is necessary, our results (i) provide real-life evidence for the notion that fatigue and boredom are temporally associated with task disengagement, and (ii) suggest that taking a short break with the smartphone may have phenomenological costs.
Most undergraduates in Mainland China receive distance education from home because of the COVID-19 outbreak. The unique physical environment of distance learning may have effects not only on student learning but also on student physical and mental health. This study reviewed related studies of the physical learning environment and its influence on learners' physical and mental health. Using factor analysis, the authors developed a questionnaire to measure students' perceptions towards their physical learning environment and their physical and mental health and verified the reliability. Regression analysis was used to explore the influence of physical environment perception on physical health and mental health perception, as well as the predictive ability of these three factors on learning satisfaction. The results of this study provide researchers and educators with insight into learners' perceptions of the physical learning environment and their physical and mental health under COVID-19 and offer suggestions for improving distance-learning satisfaction.
MOOCs as a learning approach are gaining popularity, and helping learners and instructors understand how learning engagement is constructed in a MOOC context is of increasing importance. Although previous research has undoubtedly enriched our knowledge of MOOCs, our understanding of student engagement in the MOOC context is still limited. This study adopts a sequential explanatory mixed-methods approach to examine student engagement in MOOCs from the self-determination theory (SDT) perspective. A total of 693 valid responses to a MOOC Engagement-Motivation scale were collected and 82 MOOC participants were interviewed. The results showed significant differences between the MOOC completers and non-completers with respect to the rank of motivators for enrolment and the rank of learning activities for participation. The association between perceived competence and emotional engagement was significantly higher in the MOOC completion group. The results of a multiple regression analysis indicated that the SDT model can significantly predict student engagement. Perceived competence registered the largest positive impact, and perceived relatedness had a slight negative impact on engagement. The three components of engagement can also predict learners’ perceived learning. Emotional engagement showed the largest positive impact. However, logistic regression analysis indicated that these components of engagement poorly predicted MOOC learners’ completion. Qualitative analyses of student interview data revealed three main factors that can promote learners’ SDT needs: active learning, course resources, and instructor accessibility. Implications of the findings can help MOOC designers and educators to better engage their participants.
The purpose of this study is to contribute to a better understanding of the complexity of conceptualising and measuring learner engagement in Massive Open Online Courses (MOOCs). The paper develops and validates a MOOC engagement scale (MES) to measure learner engagement. The initial questionnaire items of the scale were developed by reviewing existing related literature and validated student engagement constructs. This was followed by a modified scale development process, including two focus group interviews (n = 10), an exploratory survey (n = 12), an expert review (n = 10), a pilot survey (n = 15), an item purification study (n = 590) and a construct validation study (n = 303). The final version of the scale is made up of four dimensions: behavioural engagement, cognitive engagement, emotional engagement and social engagement. The paper concludes with recommendations for how the MES can be used to assess the pattern of engagement in MOOCs and to investigate the relationship between learner engagement and other important MOOC teaching and learning factors.
Multimodal learning analytics offer researchers
new tools and techniques to capture different types of data from
complex learning activities in dynamic learning environments.
This paper investigates high-fidelity synchronised multimodal
recordings of small groups of learners interacting from diverse
sensors that include computer vision, user-generated content,
and data from the learning objects (like physical computing
components or laboratory equipment). We processed and extracted
different aspects of the students’ interactions to answer
the following question: which features of student group work
are good predictors of team success in open-ended tasks with
physical computing? The answer to the question provides ways
to automatically identify the students at risk of not having success
during the learning activities and provides means for different
types of interventions to support and scaffold the student learning.
Background
The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual?s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual?s state can change rapidly, unexpectedly, and in his/her natural environment. PurposeDespite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods
Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusion
As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.
Big data is the emerging field where innovative technology offers new ways to extract value from the tsunami of available information. As with any emerging area, terms and concepts can be open to different interpretations. The Big Data domain is no different. This chapter examines the different definitions of “Big Data” which have emerged over the last number of years to label data with different attributes. The Big Data Value Chain is introduced to describe the information flow within a big data system as a series of steps needed to generate value and useful insights from data. The value chain enables the analysis of big data technologies for each step within the chain. The chapter explores the concept of a Big Data Ecosystem. It examines the use of the ecosystem metaphor within the business community to describe the business environment and how it can be extended to the big data context. Key stakeholders of a big data ecosystem are identified together with the challenges that need to be overcome to enable a big data ecosystem in Europe.
Although the theoretical framework of cognitive load theory has acknowledged a role for the learning environment, the specific characteristics of the physical learning environment that could affect cognitive load have never been considered, neither theoretically nor empirically. In this article, we argue that the physical learning environment, and more specifically its effects on cognitive load, can be regarded as a determinant of the effectiveness of instruction. We present an updated version of the cognitive load model of Paas and Van MerriA << nboer (Educational Psychology Review, 6:351-371, 1994a), in which the physical learning environment is considered a distinct causal factor that can interact with learner characteristics, learning-task characteristics, or a combination of both. Previous research into effects of the physical learning environment on cognitive performance that could inspire new cognitive load research is discussed, and a future research agenda is sketched.
New high-frequency data collection technologies and machine learning analysis techniques could offer new insights into learning, especially in tasks in which students have ample space to generate unique, personalized artifacts, such as a computer program, a robot, or a solution to an engineering challenge. To date most of the work on learning analytics and educational data mining has focused on online courses or cognitive tutors, in which the tasks are more structured and the entirety of interaction happens in front of a computer. In this paper, I argue that multimodal learning analytics could offer new insights into students' learning trajectories, and present several examples of this work and its educational application.
This paper aims to provide the reader with a comprehensive background for understanding current knowledge on the use of non-intrusive Mobile Sensing methodologies for emotion recognition in Smartphone devices. We examined the literature on experimental case studies conducted in the domain during the past six years (2015-2020). Search terms identified 95 candidate articles, but inclusion criteria limited the key studies to 30. We analyzed the research objectives (in terms of targeted emotions), the methodology (in terms of input modalities and prediction models) and the findings (in terms of model performance) of these published papers and categorized them accordingly. We used qualitative methods to evaluate and interpret the findings of the collected studies. The results reveal the main research trends and gaps in the field. The study also discusses the research challenges and considers some practical implications for the design of emotion-aware systems within the context of Distance Education.
Developing communication skills in higher education students could be a challenge to professors due to the time needed to provide formative feedback. This work presents RAP, a scalable system to provide automatic feedback to entry-level students to develop basic oral presentation skills. The system improves the state-of-the-art by analyzing posture, gaze, volume, filled pauses and the slides of the presenters through data captured by very low-cost sensors. The system also provides an off-line feedback report with multimodal recordings of their performance. An initial evaluation of the system indicates that the system's feedback highly agrees with human feedback and that students considered that feedback useful to develop their oral presentation skills.
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