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Learner Modeling in the Context of Caring Assessments

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Adaptive Instructional Systems (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12214))

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Abstract

Learner models maintain representations of students’ cognitive, metacognitive, affective, personality, social and perceptual skills. This information can be used to adapt the adaptive instructional system’s interactions with the student. Our work on caring assessments has provided us with an opportunity to explore learner modelling issues applied to assessment. This paper elaborates on issues such as the nature of the learner model, types of student emotions in assessment and opportunities for adaptations, and the role of individual differences in student characteristics that could inform an expanded learner model to support fine-tuned adjustments to assessment tasks. Other issues discussed include using cognitive and affective information to implement adaptations, as well as implications for reporting systems and open learner models, supporting student access to these systems, and data privacy and data security challenges.

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Authors and Affiliations

  1. Educational Testing Service, Princeton, NJ, 08541, USA

    Diego Zapata-Rivera, Blair Lehman & Jesse R. Sparks

Authors
  1. Diego Zapata-Rivera
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  2. Blair Lehman
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  3. Jesse R. Sparks
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Corresponding author

Correspondence to Diego Zapata-Rivera .

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Editors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Zapata-Rivera, D., Lehman, B., Sparks, J.R. (2020). Learner Modeling in the Context of Caring Assessments. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science(), vol 12214. Springer, Cham. https://doi.org/10.1007/978-3-030-50788-6_31

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  • DOI: https://doi.org/10.1007/978-3-030-50788-6_31

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  • Online ISBN: 978-3-030-50788-6

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