loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Linda DuHadway and Thomas C. Henderson

Affiliation: University of Utah, United States

Keyword(s): Course Transformation, Artificial Student Agents, Bayesian Network, Kalman Filter.

Related Ontology Subjects/Areas/Topics: Agent Models and Architectures ; Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bayesian Networks ; Enterprise Information Systems ; Soft Computing

Abstract: In an effort to meet the changing landscape of education many departments and universities are offering more online courses – a move that is likely to impact every department in some way (Rover et al., 2013). This will require more instructors create online courses, and we describe here how agents and dynamic Bayesian networks can be used to inform this process. Other innovations in instructional strategies are also widely impacting educators (Cutler et al., 2012) including peer instruction, flipped classrooms, problem-based learning, just-in-time teaching, and a variety of active learning strategies. Implementing any of these strategies requires changes to existing courses. We propose ENABLE, a graph-based methodology, to transform a standard linear in-class delivery approach to an on-line, active course delivery system (DuHadway and Henderson, 2015). The overall objectives are: (1) to create a set of methods to analyze the content and structure of existing learning materials that h ave been used in a synchronous, linearly structured course and provide insight into the nature and relations of the course material and provide alternative ways to organize them, (2) to provide a Bayesian framework to assist in the discovery of causal relations between course learning items and student performance, and (3) to develop some simple artificial student agents and corresponding behavior models to probe the methods’ efficacy and accuracy. In this paper, we focus on our efforts on the third point. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 52.55.55.239

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
DuHadway, L. and Henderson, T. (2016). Artificial Student Agents and Course Mastery Tracking. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-172-4; ISSN 2184-433X, SciTePress, pages 385-391. DOI: 10.5220/0005738703850391

@conference{icaart16,
author={Linda DuHadway. and Thomas C. Henderson.},
title={Artificial Student Agents and Course Mastery Tracking},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2016},
pages={385-391},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005738703850391},
isbn={978-989-758-172-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Artificial Student Agents and Course Mastery Tracking
SN - 978-989-758-172-4
IS - 2184-433X
AU - DuHadway, L.
AU - Henderson, T.
PY - 2016
SP - 385
EP - 391
DO - 10.5220/0005738703850391
PB - SciTePress