Analytics in Authentic Learning

Kumar, Vive; Kinshuk, Kinshuk; Pinnell, Colin; Paulmani, Geetha

doi:10.1007/978-981-10-5930-8_6

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…Learning input theory explains the relationship between learning behaviour and learning performance 26 and states that learning behaviour is a key factor affecting learning performance and an important indicator for predicting learning performance 27 . Concurrently, many studies have confirmed that there is a significant correlation between student online activities and academic performance 28 , 29 , and observing learning activities at a finer-grained level can strengthen the grasp of learning conditions and promote constructive learning 30 . Therefore, many researchers have explored the correlation between e-learning behaviour and learning performance, and used e-learning behaviour to predict learning performance.…”

Section: Related Workmentioning

confidence: 92%

Predicting students’ performance in e-learning using learning process and behaviour data

Qiu

Zhang

Sheng

et al. 2022

Sci Rep

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E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification methods do not fully consider the process of learning, we also propose an online behaviour classification model based on the e-learning process called the process-behaviour classification (PBC) model. Experimental results with the Open University Learning Analytics Dataset (OULAD) show that the learning performance predictor based on the BCEP prediction framework has a good prediction effect, and the performance of the PBC model in learning performance prediction is better than traditional classification methods. We construct an e-learning performance predictor from a new perspective and provide a new solution for the quantitative evaluation of e-learning classification methods.

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Section: Related Workmentioning

confidence: 92%

Predicting students’ performance in e-learning using learning process and behaviour data

Qiu

Zhang

Sheng

et al. 2022

Sci Rep

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“…Primary data were gathered from those who were anticipated to exhibit excessive e-learning usage [131,132] and who widely embraced e-learning [133], using both manual and online questionnaire methods due to geographic restrictions, for faster dispersion, and to minimize the issue of invalid or incomplete information [134]. Nine concepts were assessed using a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly disagree) in order to study the adoption of e-learning.…”

Section: Measurement Developmentmentioning

confidence: 99%

Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability

Almulla

Al-Rahmi

2023

Sustainability

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E-learning is expected to become a common teaching and learning approach in educational institutions in the near future; thus, the success of e-learning initiatives must be ensured in order to make this a sustainable mode of learning. In order to improve students’ learning performance through the use of e-learning in Saudi Arabia’s higher education, it was the objective of this paper to examine the relationships between social cognitive theory and learning input factors and the reflective thinking and inquiry learning style as well as the indirect effects of student problem-solving and critical thinking skills. As a result, this study thoroughly assessed the social cognitive theory that is currently in use, along with learning input components and situational factors that should be carefully taken into account while introducing an online education system into Saudi Arabia’s top universities as a way of ensuring learning sustainability. As a result, 294 university students completed a questionnaire that served as the initial dataset for the research study, and the proposed conceptual model was comprehensively assessed using SEM. The research results demonstrated that the inquiry style of learning and reflective thinking have always had a significant impact on the social involvement, human engagement, social power, social identity, and social support. Similar findings were obtained regarding the impact of problem-solving and critical thinking skills on the inquiry-based learning approach and reflective thinking. Thus, students’ ability to learn in Saudi Arabia’s higher education is greatly influenced by their ability to solve problems and think critically. Therefore, it is almost certain that this research study will aid university policy makers in their decision on whether to fully deploy an online learning system as a way of ensuring learning sustainability at educational institutions throughout Saudi Arabia.

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“…2Write (Boulanger et al, 2018;Mitchnick et al, 2017;Kumar, Kinshuk, Pinnell and Paulmani, 2017;Kinshuk, 2017) is another writing analytics tool for enhancing English writing competence. It is compatible with learning management systems such as Moodle.…”

Section: Writing Analytics -Mi-writer and 2writementioning

confidence: 99%

“…Teachers can use such visual aids to dynamically check student competence levels and detect their pedagogic needs. More importantly, both teachers and learners can perceive learning as a highly active and dynamic growth process, rather than seeing it as a single "snap shot" (Kumar, Kinshuk, Pinnell and Paulmani, 2017). Teachers and students can follow a learning timeline and see how learning has influenced performance over a certain period.…”

Section: Dashboards For Teachersmentioning

confidence: 99%

“…LA has the potential to create better learning experiences customized to individual preferences, strengths and needs (Clow, 2013;Siemens, 2012). The customization of learning experiences can be intensified when all learning related activities are observed at finer levels of granularityincluding episodes of study by students, episodes of assessment preparation and completion, and episodes of teachingto generate awareness/insights about what learners know and how to promote constructive learning (Kumar, Kinshuk, Pinnell and Paulmani, 2017;. Boulanger et al (2015) examined Smart LA using a case study on the Smart LA framework SCALE, and its effectiveness in improving student learning outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Advancing learning through smart learning analytics: a review of case studies

Kinshuk

Kumar

2018

AAOUJ

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Vivekanandan KumarAthabasca University, Athabasca, Canada Abstract Purpose -Smart learning analytics (Smart LA) -i.e. the process of collecting, analyzing and interpreting data on how students learn -has great potentials to support opportunistic learning and offer better -and more personalized -learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases. Design/methodology/approach -The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.Findings -Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction. Originality/value -Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

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Analytics in Authentic Learning

Cited by 4 publications

References 7 publications

Predicting students’ performance in e-learning using learning process and behaviour data

Predicting students’ performance in e-learning using learning process and behaviour data

Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability

Advancing learning through smart learning analytics: a review of case studies

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