Technology enabled categorisation of learners for improved support in experiential learning.

James, Nicole
(2021) Technology enabled categorisation of learners for improved support in experiential learning. Doctor of Education thesis, University of Liverpool.

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The purpose of this thesis is to examine which data captured by experiential learning technology can be used to understand more about students’ perspectives, mindsets and skills. The objective is to examine how technology-enabled real-time analysis of learner data can be used by learning facilitators and instructional designers to improve the practice of experiential learning in higher education institutions. The study adopts an anti-positivist perspective that acknowledges habit as a driver of deterministic behaviour and that deterministic behaviour can be examined using scientific methods. The data used in this research is retrospectively de-identified student learning data captured by an experiential learning technology which has been used to structure and support the facilitation of an experiential business project program. The research findings outline the quantitative outcomes followed by an integrative qualitative discussion that explores how the findings could be used to inform the practice of experiential learning design and facilitation. Specifically the methodology outlines: the experiential business project program design, the classification of learning tasks into independent variable categories, and the results of student responses to three surveys. The three surveys being: the Revised Implicit Theories of Intelligence Survey, Revised Two Factor Study Process Questionnaire and a learning history survey and the manner in which these surveys were dummy coded into dependent variables, with a detailed description of how the regression analysis is conducted. The results section presents and examines the five regression models developed. The purpose of the examination is to explore the extent to which learner data from an experientially developed learning technology could be used to understand more about students’ perspectives, mindsets and skills. The integrative discussion examines each of the three research questions explicitly. The discussion focused on research question one examines the nature of the learning tasks that have a significant relationship with one or more of the learning theory based dependent variables. It investigates whether there is an alignment between what is known about the nature of learners who exhibit or employ a particular mindset, approach to learning or learning history and the learning task categories use as independent variables in the five regression models presents in the results. The discussion focused on research question two examines what additional learning data could be captured to improve the predictive power of the five regression models. The discussion focused on research question three examines how displaying predictive insights, using learner data, alongside learning theory insights could be used by instructional designers and learning facilitators. The discussion explores how facilitators and learning designers could use the information to customise facilitator support, aid in the development of incentives that encourage learners to engage with learning content that they do not naturally lean towards and support the adaption of learning content to align better with a learner's motives. This study further proposes an example of the benefits of integrating learning analytics and learning theory, how learning theory based analysis could enable more use of experiential learning within higher education institutions, enable experiential learning facilitators to provide more tailored support of students during experiential learning programs and how the results of the analysis could help students extract more of the benefits from the available learning out of experiential learning programs.

Item Type: Thesis (Doctor of Education)
Divisions: Faculty of Humanities and Social Sciences
Depositing User: Symplectic Admin
Date Deposited: 04 May 2021 14:52
Last Modified: 18 Jan 2023 22:51
DOI: 10.17638/03119938
  • Kelm, Kathleen
  • Strivens, Janet
  • Gough, Martin