{"id":65,"date":"2021-01-07T16:54:32","date_gmt":"2021-01-07T16:54:32","guid":{"rendered":"https:\/\/olgarotar.com\/blog\/?page_id=65"},"modified":"2024-09-18T11:40:00","modified_gmt":"2024-09-18T11:40:00","slug":"research","status":"publish","type":"page","link":"https:\/\/olgarotar.com\/blog\/index.php\/research\/","title":{"rendered":"Research"},"content":{"rendered":"\t\t
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Research <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\nComputer-assisted phenomenography: testing the use of NLP methods for analysis of students' conceptual understanding\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The volume and complexity of qualitative data generated by students, such as reflections on threshold concepts, present significant challenges for educators seeking to provide meaningful feedback. Advanced methods for analyzing large qualitative datasets, including natural language processing (NLP) (Kovanovi\u0107 et al., 2018; Prieto et al., 2019), offer promising solutions. However, existing studies often lack robust theoretical foundations, making it difficult to systematically understand students’ conceptual (mis)understandings. This project proposes a groundbreaking approach by integrating phenomenography and variation theory with advanced NLP techniques, specifically topic modeling, to analyze students’ reflections on threshold concepts. While phenomenography and variation theory have been applied to studying conceptual understanding (Han & Ellis, 2019; Prosser, 1994; Reed, 2006), their combination with advanced analytical methods remains unexplored. This project aims to fill this gap by using theory-supported topic modeling to identify variations in students’ conceptual understanding and uncover mechanisms behind these variations. The project objectives are:<\/p>\n

1. To identify and code critical disciplinary concepts needing curriculum design attention based on threshold concepts.
2. To analyze variations in student understanding using NLP topic modeling and variation theory.
3. To create a phenomenographic model representing a nested hierarchy of students’ conceptual understanding.
4. To explore how different reflective approaches correlate with levels of conceptual understanding.
5. To develop and test a novel computer-assisted approach for analyzing students’ conceptual understanding, integrating advanced NLP techniques with phenomenography.<\/p>

By combining data-driven analytics with phenomenography and variation theory, this study could lead to better-informed instructional strategies and more personalised educational interventions. Additionally, it will challenge traditional qualitative research norms, proposing innovative workflows for analyzing qualitative educational data.<\/span><\/p>\n

The project has been accepted to present at the EARLI 2023 conference. The project description can be found l here<\/a>.  <\/p>\n

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Labour market research<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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My interest in labour market research includes understanding how decision-making processes unfold, examining the impact of incentive contracts on job satisfaction, and exploring the implications of precarious working conditions.<\/p>

Traditional economic models assume rational behaviour on the labour market, but research from behavioural economics and neuroeconomics shows that individuals deviate from rationality due to bounded cognitive abilities, risk aversion, and emotions. I am interested in investigating how these elements influence decisions in labour markets, specifically in regard to job offers and salary negotiations. Additionally, I am interested in understanding gender differences in decision-making in the labour market context, to contribute to explaining experimental data on risky choices and provide evidence on whether complex job roles may be designed to exclude certain groups, thereby reinforcing labour market inequalities.<\/p>

I investigate how incentive contracts, like pay-for-performance schemes, impact academics\u2019 job satisfaction. I aim to understand whether intrinsic factors affect job satisfaction in the presence of the wage, and whether the enrollment into the pay-for-performance enhances or diminishes employee satisfaction.<\/p>

I also study the rise of precarious employment in academia, such as part-time and fixed-term contracts. I\u2019m particularly interested in how these contracts are distributed across different academic roles and levels of seniority, and how they affect various groups, including gender.<\/p>

My research aims to highlight disparities and inform HR practices and policy reforms to address labour market inequalities.<\/p>

Research outcomes<\/span><\/p>