Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic

Jain, Gauri; Gurupur, Varadraj P.; Schroeder, Jennifer; Faulkenberry, E. D.

doi:10.1109/tlt.2014.2330297

Cited by 49 publications

(28 citation statements)

References 24 publications

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“…45 Lastly, the implementation of artificial intelligence (AI) in LA-oriented systems can identify learners' understanding and predict their behaviour. 46 Recent progresses already provide the means to 'real-time' feedback-response recalibration of content, with the system automatically providing learning activity and learning adaption recommendations to improve learners' learning motivation and learning success. 47 …”

Section: 40mentioning

confidence: 99%

“…44,45 Also, AI supporting systems, by analysing patterns and learners metric data, can contribute to individual study effectiveness. 46 The continuous development of supporting pedagogical strategies, which reflect Medical Education's increasingly complexity and mutability, is expected. 49,53 Nevertheless, the consolidation and assimilation of the blended learning in Anatomy and Medical Education will depend on the continuous evaluation of the implemented methodologies.…”

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confidence: 99%

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Rethinking Anatomy: How to Overcome Challenges of Medical Education’s Evolution

et al. 2017

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analysing the advantages of the introduction and diversification of pedagogical approaches in Anatomy Education.Results: Anatomy Education's status quo is characterized by: less available teaching time, increasing demands from radiology and endoscopy imaging and other invasive and non-invasive medical techniques, increasing number of medical students and other logistical restrains exposed by the current Medical Education scenario. The traditional learning approach, mainly based on cadaveric dissection, is drifting to complementary newer technologies -such as 3D models or 2D/3D digital imaging -to examine the anatomy of the human body. Also, knowledge transfer is taking different channels, as learning management systems, social networks and computer-assisted learning and assessment are assuming relevant roles. Discussion: The future holds promising approaches for education models. The development of Artificial Intelligence, Virtual Reality and Learning Analytics could provide analytic tools towards a real-time and personalized learning process. Conclusion: A reflection on Anatomy Education, as a comprehensive model, allows us to understand Medical Education's complexity.Therefore, the present Medical Education context favours a blended learning approach, in which multi-modality pedagogical strategies may become the landmark.

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Section: 40mentioning

confidence: 99%

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confidence: 99%

Rethinking Anatomy: How to Overcome Challenges of Medical Education’s Evolution

et al. 2017

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“…Following [10][11][12], the concept maps support students in understanding the novel topics by mapping the links among new and previously studied domains.…”

Section: Education Research Internationalmentioning

confidence: 99%

Concept Maps for Teaching, Learning, and Assessment in Electronics

Vodovozov

Raud²

2015

Education Research International

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The paper describes how to employ the concept mapping technology in engineering education in the field of Electronics. The overall knowledge domain ontology in the field is shown. It is underlined that the concept maps serve as a suitable tool to support instructors in promoting students' comprehension of the studying material and in improving their understanding of new concepts. Introduction of an original educational thesaurus is proposed. Such a thesaurus helps learners to see what they have acquired from the lessons. It supports them in making connections between new and prior concepts and reinforces knowledge integration by such a promotion. The developed concept maps are regarded as a valuable instrument of many assessment procedures. They represent learners' knowledge providing informative and reflective feedbacks tailored to learners' personal styles and requests.

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“…Concept maps are visual representations of data stored in a system, traditionally used for expressing knowledge in both a declarative and procedural sense [35]. Most concept mapping tools have been previously used to express knowledge in a teaching environment to evaluate student learning of various topics [36,37]. An example is provided in ▶ Figure 2.…”

Section: Concept Mapping and Data Handlingmentioning

confidence: 99%

A New Paradigm to Analyze Data Completeness of Patient Data

Nasir¹,

Gurupur²,

Liu³

2016

Appl Clin Inform

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SummaryBackground: There is a need to develop a tool that will measure data completeness of patient records using sophisticated statistical metrics. Patient data integrity is important in providing timely and appropriate care. Completeness is an important step, with an emphasis on understanding the complex relationships between data fields and their relative importance in delivering care. This tool will not only help understand where data problems are but also help uncover the underlying issues behind them. Objectives: Develop a tool that can be used alongside a variety of health care database software packages to determine the completeness of individual patient records as well as aggregate patient records across health care centers and subpopulations. Methods: The methodology of this project is encapsulated within the Data Completeness Analysis Package (DCAP) tool, with the major components including concept mapping, CSV parsing, and statistical analysis. Results:The results from testing DCAP with Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) data show that this tool is successful in identifying relative data completeness at the patient, subpopulation, and database levels. These results also solidify a need for further analysis and call for hypothesis driven research to find underlying causes for data incompleteness. Conclusion: DCAP examines patient records and generates statistics that can be used to determine the completeness of individual patient data as well as the general thoroughness of record keeping in a medical database. DCAP uses a component that is customized to the settings of the software package used for storing patient data as well as a Comma Separated Values (CSV) file parser to determine the appropriate measurements. DCAP itself is assessed through a proof of concept exercise using hypothetical data as well as available HCUP SID patient data.

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Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic

Cited by 49 publications

References 24 publications

Rethinking Anatomy: How to Overcome Challenges of Medical Education’s Evolution

Rethinking Anatomy: How to Overcome Challenges of Medical Education’s Evolution

Concept Maps for Teaching, Learning, and Assessment in Electronics

A New Paradigm to Analyze Data Completeness of Patient Data

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