| 1. |
- Farazouli, Alexandra, et al.
(författare)
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Hello GPT! Goodbye home examination? An exploratory study of AI chatbots impact on university teachers' assessment practices
- 2023
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Ingår i: Assessment & Evaluation in Higher Education. - 0260-2938 .- 1469-297X.
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Tidskriftsartikel (refereegranskat)abstract
- AI chatbots have recently fuelled debate regarding education practices in higher education institutions worldwide. Focusing on Generative AI and ChatGPT in particular, our study examines how AI chatbots impact university teachers' assessment practices, exploring teachers' perceptions about how ChatGPT performs in response to home examination prompts in undergraduate contexts. University teachers (n = 24) from four different departments in humanities and social sciences participated in Turing Test-inspired experiments, where they blindly assessed student and ChatGPT-written responses to home examination questions. Additionally, we conducted semi-structured interviews in focus groups with the same teachers examining their reflections about the quality of the texts they assessed. Regarding chatbot-generated texts, we found a passing rate range across the cohort (37.5 - 85.7%) and a chatbot-written suspicion range (14-23%). Regarding the student-written texts, we identified patterns of downgrading, suggesting that teachers were more critical when grading student-written texts. Drawing on post-phenomenology and mediation theory, we discuss AI chatbots as a potentially disruptive technology in higher education practices.
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| 2. |
- Farazouli, Alexandra
(författare)
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School choice and private schooling : a comparative case-study between Greece and Sweden
- 2019
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Ingår i: 11Th World Conference On Educational Sciences (WCES-2019). - : SCIENCEPARK SCI, ORGANIZATION & COUNSELING LTD. ; , s. 213-223
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Konferensbidrag (refereegranskat)abstract
- Over the past three decades, privatisation and school choice have been introduced and embodied in the vocabulary of several national education policies. This study aiming to examine the phenomenon of private schooling and the factors that affect parental school choice outlined a comprehensive framework of the national policies about private schools and school choice in Greece and Sweden. The case study design of the research provided an in-depth exploration of the two national contexts, enriching the study with empirical data. Twenty semi-structured interviews with education professionals and parents from both countries shed light on the reasons behind the school choice towards private schools. Regarding the findings of the research, several kinds of educational inequalities and social segregation were identified because of the fact that not all parents have access to school choice under equal terms.
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| 3. |
- Ljungman, Jimmy, et al.
(författare)
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Automated Grading of Exam Responses : An Extensive Classification Benchmark
- 2021
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Ingår i: Discovery Science. - Cham : Springer Nature. - 9783030889418 - 9783030889425 ; , s. 3-18
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Konferensbidrag (refereegranskat)abstract
- Automated grading of free-text exam responses is a very challenging task due to the complex nature of the problem, such as lack of training data and biased ground-truth of the graders. In this paper, we focus on the automated grading of free-text responses. We formulate the problem as a binary classification problem of two class labels: low- and high-grade. We present a benchmark on four machine learning methods using three experiment protocols on two real-world datasets, one from Cyber-crime exams in Arabic and one from Data Mining exams in English that is presented first time in this work. By providing various metrics for binary classification and answer ranking, we illustrate the benefits and drawbacks of the benchmarked methods. Our results suggest that standard models with individual word representations can in some cases achieve competitive predictive performance against deep neural language models using context-based representations on both binary classification and answer ranking for free-text response grading tasks. Lastly, we discuss the pedagogical implications of our findings by identifying potential pitfalls and challenges when building predictive models for such tasks.
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