The phenomenon of transfer—our ability to perform novel tasks by generalizing from past experiences—has long captivated theorists and practitioners. As educators it is essential for us to understand what types of learning best promote transfer and to structure our curricula accordingly. With that goal in mind, this dissertation outlines two lines of research.
For the first line of research I adopted an experimental approach in the domain of problem solving, examining a training technique whereby the learner solves practice problems for the same principle in dissimilar contexts as opposed to highly similar contexts. The key finding was that contextual variability improved transfer outcomes when a set of training problems were solved spaced in time (akin to a closed-book test), but not when prior training problems and their solutions remained visible throughout training (akin to an open-book test). This finding suggests that contextual variability during training can be beneficial because it forces the learner to more effortfully recall what they have learned in the past.
For the second line of research I then adopted a correlational approach, investigating a ubiquitous self-report inventory, the Study Process Questionnaire (SPQ), which is meant to quantify student learning approaches to predict educational outcomes. However, the SPQ’s predictive validity has recently been challenged because deep learning and its corresponding outcomes remain poorly defined. To tackle this measurement issue, my colleagues and I operationally defined outcome measures in real university courses to tap more precisely into transfer of learning. Across several studies we found limited evidence for the SPQ’s ability to predict transfer outcomes, leading us to suggest that educators and researchers should be more cautious about using this self-report inventory to characterize student learning. / Thesis / Doctor of Philosophy (PhD) / A central goal of education is to equip students with ‘flexible’ knowledge, enabling them to transfer the far-reaching principles they have learned to solve new, real-world problems. But what conditions of training are most conducive to transfer? One understudied technique involves being tested on the same principle in dissimilar contexts. The experiments reported in Chapter 2 provide evidence for this training technique in the domain of problem solving. Aside from direct interventions, another approach has been to measure individual differences among students to predict how much they engage in “deep learning”—a process closely associated with transfer. However, four correlational studies in Chapters 3 and 4 revealed little support for this approach, highlighting the difficulty of characterizing learning strategies using self-reports. In sum, this shows promise for interventions involving repeated testing in dissimilar contexts, but little promise for a self-report inventory meant to capture individual differences in student learning.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/25897 |
| Date | January 2020 |
| Creators | LoGiudice, Andrew B. |
| Contributors | Monteiro, Sandra, Psychology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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