Development of Real-Time Learning Analytics Using Scraping and Pivot Tables

2020 ◽  
pp. 311-318
Author(s):  
Konomu Dobashi
Keyword(s):  
Real Time ◽  
Learning Analytics

Context-Based Data Model for Effective Real-Time Learning Analytics

2020 ◽  
Vol 13 (4) ◽  
pp. 790-803
Author(s):  
Kai Liu ◽  
Sivanagaraja Tatinati ◽  
Andy W. H. Khong
Keyword(s):  
Real Time ◽  
Data Model ◽  
Learning Analytics

scholarly journals Artificial intelligence, real-time feedback and workplace learning analytics to support in situ complex problem-solving: a commentary

2020 ◽  
Vol 37 (5) ◽  
pp. 267-277
Author(s):  
Maarten de Laat ◽  
Srecko Joksimovic ◽  
Dirk Ifenthaler
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real Time ◽  
Workplace Learning ◽  
Learning Analytics ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Feedback Systems ◽  
Content Type

PurposeTo help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).Design/methodology/approachComplex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).FindingsIn this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.Originality/valueThe commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

scholarly journals Mining interactions in immersive learning environments for real-time student feedback

2013 ◽  
Vol 29 (2) ◽  
Author(s):  
Gregor Kennedy ◽  
Ioanna Ioannou ◽  
Yun Zhou ◽  
James Bailey ◽  
Stephen O'Leary
Keyword(s):  
Real Time ◽  
Learning Environments ◽  
Learning Analytics ◽  
Learning Systems ◽  
Student Feedback ◽  
Formative Feedback ◽  
Digital Data ◽  
Online Interactions ◽  
Use Of Data ◽  
Hidden Patterns

<p>The analysis and use of data generated by students’ interactions with learning systems or programs – learning analytics – has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in students’ online interactions that can be meaningfully interpreted and then fed back to students in a way that supports their learning. In this paper we present an investigation of how the digital data records of students’ interactions within an immersive 3D environment can be mined, modeled and analysed, to provide real-time formative feedback to students as they complete simulated surgical tasks. The issues that emerged in this investigation as well as areas for further research and development are discussed.</p>

scholarly journals Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher–AI Complementarity

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Kenneth Holstein ◽  
Bruce M. McLaren ◽  
Vincent Aleven
Keyword(s):  
Real Time ◽  
Real World ◽  
Learning Analytics ◽  
Educational Technologies ◽  
Design Development ◽  
Awareness Tool ◽  
Classroom Evaluation ◽  
Support Teacher ◽  
K 12 ◽  
Time Awareness

Involving stakeholders throughout the creation of new educational technologies can help ensure their usefulness and usability in real-world contexts. However, given the complexity of learning analytics (LA) systems, it can be challenging to meaningfully involve non-technical stakeholders throughout their design and development. This article reports on the iterative co-design, development, and classroom evaluation of Konscia, a wearable, real-time awareness tool for teachers working in AI-enhanced K-12 classrooms. In the process, we argue that the co-design of LA systems requires new kinds of prototyping methods. We introduce one of our own prototyping methods, REs, to address unique challenges of co-prototyping LA tools. This work presents the first end-to-end demonstration of how non-technical stakeholders can participate throughout the whole design process for a complex LA system—from early generative phases to the selection and tuning of analytics to evaluation in real-world contexts. We conclude by providing methodological recommendations for future LA co-design efforts.

scholarly journals Real-time learning analytics system for improvement of on-site lectures

2018 ◽  
Vol 15 (4) ◽  
pp. 314-331 ◽  
Author(s):  
Atsushi Shimada ◽  
Shin’ichi Konomi ◽  
Hiroaki Ogata
Keyword(s):  
Real Time ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Large Scale ◽  
Feedback System ◽  
Learning System ◽  
Content Type ◽  
New Strategy ◽  
Teachers And Students ◽  
Scale Experiment

Purpose The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc. Design/methodology/approach The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation. Findings Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences. Originality/value Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately.

scholarly journals Learning Analytics: Learning to Think and Make Decisions

2019 ◽  
Vol 16 ◽  
Author(s):  
Airina Volungevičienė ◽  
Josep Maria Duart ◽  
Justina Naujokaitienė ◽  
Giedrė Tamoliūnė ◽  
Rita Misiulienė
Keyword(s):  
Online Learning ◽  
Real Time ◽  
Learning Analytics ◽  
Online Teaching ◽  
Teacher Inquiry ◽  
Learning Design ◽  
Teaching Process ◽  
Research Results ◽  
Learning And Teaching ◽  
Specific Analysis

The research aims at a specific analysis of how learning analytics as a metacognitive tool can be used as a method by teachers as reflective professionals and how it can help teachers learn to think and come down to decisions about learning design and curriculum, learning and teaching process, and its success. Not only does it build on previous research results by interpreting the description of learning analytics as a metacognitive tool for teachers as reflective professionals, but also lays out new prospects for investigation into the process of learning analytics application in open and online learning and teaching. The research leads to the use of learning analytics data for the implementation of teacher inquiry cycle and reflections on open and online teaching, eventually aiming at an improvement of curriculum and learning design. The results of the research demonstrate how learning analytics method can support teachers as reflective professionals, to help understand different learning habits of their students, recognize learners’ behavior, assess their thinking capacities, willingness to engage in the course and, based on the information, make real time adjustments to their course curriculum.

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