English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The accurate classification of student help requests with respect to the typeof help being sought can enable the tailoring of effective responses.Automatically classifying such requests is non-trivial, but large languagemodels (LLMs) appear to offer an accessible, cost-effective solution. Thisstudy evaluates the performance of the GPT-3.5 and GPT-4 models for classifyinghelp requests from students in an introductory programming class. In zero-shottrials, GPT-3.5 and GPT-4 exhibited comparable performance on most categories,while GPT-4 outperformed GPT-3.5 in classifying sub-categories for requestsrelated to debugging. Fine-tuning the GPT-3.5 model improved its performance tosuch an extent that it approximated the accuracy and consistency acrosscategories observed between two human raters. Overall, this study demonstratesthe feasibility of using LLMs to enhance educational systems through theautomated classification of student needs.

Efficient Classification of Student Help Requests in Programming Courses Using Large Language Models