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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

A Comparative Evaluation of Matrix Training Arrangements

Cliett, Terra N. 05 1900 (has links)
A common goal of instructional techniques is to teach skills effectively and efficiently. Matrix training techniques are both effective and efficient as they allow for the emergence of untrained responding to novel stimulus arrangements, a phenomenon known as recombinative generalization. However, it is unclear which type of matrix arrangement best promotes recombinative generalization. The current study compared two common matrix training approaches, an overlapping (OV) design and a non-overlapping (NOV) design, with respect to arranging relations targeted for training. We conducted a replication evaluation of a Wilshire and Toussaint study, and taught two typically-developing preschoolers compound object-action labels in Spanish and used either an OV or NOV matrix training design. Results from both studies demonstrated the participant trained with an OV design produced recombinative generalization and participants trained with a NOV design produced significantly low levels of emergence or none at all. These results suggest that an OV matrix design facilitates recombinative generalization more effectively than a NOV design. Implications for instructional arrangements are discussed.
2

Generative Language Models for Automated Programming Feedback

Hedberg Segeholm, Lea, Gustafsson, Erik January 2023 (has links)
In recent years, Generative Language Models have exploded into the mainstream with household names like BERT and ChatGPT, proving that text generation could have the potential to solve a variety of tasks. As the number of students enrolled into programming classes has increased significantly, providing adequate feedback for everyone has become a pressing logistical issue. In this work, we evaluate the ability of near state-of-the-art Generative Language Models to provide said feedback on an automated basis. Our results show that the latest publicly available model GPT-3.5 has a significant aptitude for finding errors in code while the older GPT-3 is noticeably more uneven in its analysis. It is our hope that future, potentially fine-tuned models could help fill the role of providing early feedback for beginners, thus significantly alleviating the pressure put upon instructors.

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