SearcharxivSearch

arXiv subjects

Jelena Jovanovic

Publications and source records attributed to Jelena Jovanovic.

5 recordsLinked to original sources

Profiling cognitive offloading in LLM-mediated synthesis writing: Volume vs. content

This study compares two approaches to profiling how learners offload cognitive activity to LLMs during a synthesis writing task. Drawing on Salomon's distributed cognition and the Kintsch and van Dijk model of text comprehension, the study operationalises offloading to an LLM in two ways: as a volume of LLM use and as content of what is offloaded, both along with prior knowledge. Data from 97 university students interacting with a general-purpose LLM via a custom interface were analysed using k-means clustering. To capture the content of offloading, their prompts were interpreted as to who performs the activity (active or passive) and at what level of comprehension (local or global). Volume-based profiling (k=4) differentiated learners primarily by prior knowledge, with volume negatively associated with essay authorship. Content-based profiling (k=5) revealed qualitatively distinct patterns of offloading, from vocabulary clarification to active direction of structuring and generation to passive delegation of comprehension at both levels. These patterns reflect different fragmentation of the cognitive process, with differences in learning strategies, behavioural markers, and essay authorship. Combining volume and content of offloading could improve future analyses on how LLM use redistributes cognitive activity and its effects on learners.

cs.HC

Protecting and Promoting Human Agency in Education in the Age of Artificial Intelligence

Human agency is crucial in education and increasingly challenged by the use of generative AI. This meeting report synthesizes interdisciplinary insights and conceptualizes four aspects that delineate human agency: human oversight, AI-human complementarity, AI competencies, and relational emergence. We explore practical dilemmas for protecting and promoting agency, focusing on normative constraints, transparency, and cognitive offloading, and highlight key tensions and implications to inform ethical and effective AI integration in education.

cs.HC

Advancing privacy in learning analytics using differential privacy

This paper addresses the challenge of balancing learner data privacy with the use of data in learning analytics (LA) by proposing a novel framework by applying Differential Privacy (DP). The need for more robust privacy protection keeps increasing, driven by evolving legal regulations and heightened privacy concerns, as well as traditional anonymization methods being insufficient for the complexities of educational data. To address this, we introduce the first DP framework specifically designed for LA and provide practical guidance for its implementation. We demonstrate the use of this framework through a LA usage scenario and validate DP in safeguarding data privacy against potential attacks through an experiment on a well-known LA dataset. Additionally, we explore the trade-offs between data privacy and utility across various DP settings. Our work contributes to the field of LA by offering a practical DP framework that can support researchers and practitioners in adopting DP in their works.

cs.CR

Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics

Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse that current solutions struggle to address. Synthetic data emerges as a potential remedy, offering robust privacy protection. However, prior LA research on synthetic data lacks thorough evaluation, essential for assessing the delicate balance between privacy and data utility. Synthetic data must not only enhance privacy but also remain practical for data analytics. Moreover, diverse LA scenarios come with varying privacy and utility needs, making the selection of an appropriate synthetic data approach a pressing challenge. To address these gaps, we propose a comprehensive evaluation of synthetic data, which encompasses three dimensions of synthetic data quality, namely resemblance, utility, and privacy. We apply this evaluation to three distinct LA datasets, using three different synthetic data generation methods. Our results show that synthetic data can maintain similar utility (i.e., predictive performance) as real data, while preserving privacy. Furthermore, considering different privacy and data utility requirements in different LA scenarios, we make customized recommendations for synthetic data generation. This paper not only presents a comprehensive evaluation of synthetic data but also illustrates its potential in mitigating privacy concerns within the field of LA, thus contributing to a wider application of synthetic data in LA and promoting a better practice for open science.

cs.CR

Reviewer assignment problem: A scoping review

Peer review is an integral component of scientific research. The quality of peer review, and consequently the published research, depends to a large extent on the ability to recruit adequate reviewers for submitted papers. However, finding such reviewers is an increasingly difficult task due to several factors, such as the continuous increase both in the production of scientific papers and the workload of scholars. To mitigate these challenges, solutions for automated association of papers with "well matching" reviewers - the task often referred to as reviewer assignment problem (RAP) - have been the subject of research for thirty years now. Even though numerous solutions have been suggested, to our knowledge, a recent systematic synthesis of the RAP-related literature is missing. To fill this gap and support further RAP-related research, in this paper, we present a scoping review of computational approaches for addressing RAP. Following the latest methodological guidance for scoping reviews, we have collected recent literature on RAP from three databases (Scopus, Google Scholar, DBLP) and, after applying the eligibility criteria, retained 26 studies for extracting and synthesising data on several aspects of RAP research including: i) the overall framing of and approach to RAP; ii) the criteria for reviewer selection; iii) the modelling of candidate reviewers and submissions; iv) the computational methods for matching reviewers and submissions; and v) the methods for evaluating the performance of the proposed solutions. The paper summarises and discusses the findings for each of the aforementioned aspects of RAP research and suggests future research directions.

cs.IR