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 Data Warehouse Schemas using Multidimensional Data Model for Retail

2017 ◽  
Vol 2 (1) ◽  
pp. 84
Author(s):  
Kheri Arionadi Shobirin ◽  
Adi Panca Saputra Iskandar ◽  
Ida Bagus Alit Swamardika
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Data Warehouse ◽  
Data Model ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Complex Query ◽  
On Line ◽  
Analytical Processing ◽  
Operational Data

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

An Integrated Process Planning System Architecture for Machining and On-Machine Inspection

2008 ◽  
Author(s):  
Yaoyao F. Zhao ◽  
Xun W. Xu ◽  
Sheng Q. Xie ◽  
Tom R. Kramer ◽  
Fred M. Proctor ◽  
...  
Keyword(s):  
Real Time ◽  
Process Planning ◽  
System Architecture ◽  
Data Model ◽  
Machining Process ◽  
Planning System ◽  
Integrated Process ◽  
Process Planning System ◽  
Inspection Process ◽  
Manufacturing Chain

Inspection is an essential part of the entire manufacturing chain providing measurement feedback to the process planning system. Fully automated machining requires automatic inspection process planning and real-time inspection results feedback. As inspection process planning is still based on G&M codes containing low-level information or vendor-specific bespoke routines, inspection process planning is mostly isolated from machining process planning. With the development of new data model standards STEP and STEP-NC providing high-level product information for the entire manufacturing chain, it is achievable to combine machining and inspection process planning to generate optimal machining and inspection sequences with real-time measurement results feedback. This paper introduces an integrated process planning system architecture for combined machining and inspection. In order to provide real-time inspection feedback, On-Machine Inspection (OMI) is chosen to carry out inspection operations. Implementation of the proposed architecture has been partially carried out with a newly developed data model and interpreter software. A case study was carried out to test the feasibility of the proposed architecture.

Engineering Three-Dimensional Digital Modeling Technology Based on the OGRE Graphics Engine

2012 ◽  
Vol 263-266 ◽  
pp. 1407-1413
Author(s):  
Lu Shuang Wei ◽  
Ying Wang
Keyword(s):  
Real Time ◽  
Data Model ◽  
Field Model ◽  
Three Dimensional ◽  
Time Model ◽  
Modeling Technology ◽  
Time Simulation ◽  
Digital Modeling ◽  
Graphic Construction ◽  
Graphics Engine

In order to solve the problem that the engineering three-dimensional data field model in the virtual reality environment cannot be modified in real time and cannot carry out dynamic simulation, this paper proposes the skeleton grid modeling technology and establishes an engineering three-dimensional data model based on the OGRE graphics engine, which achieves a data-driven dynamic three-dimensional graphic construction. The technology has the ability to modify the real-time model grid geometry and topological characteristics, and provides the possibility of real-time simulation for large projects.

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.

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