Author name cluster

Avi Segal

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

ECAI Conference 2025 Conference Paper

Detecting Struggling Student Programmers Using Proficiency Taxonomies

Noga Schwartz
Roy Fairstein
Avi Segal
Kobi Gal

Early detection of struggling student programmers is crucial for providing them with personalized support. While multiple AI-based approaches have been proposed for this problem, they do not explicitly reason about students’ programming skills in the model. This study addresses this gap by developing in collaboration with educators a taxonomy of proficiencies that categorizes how students solve coding tasks and is embedded in the detection model. Our model, termed the Proficiency Taxonomy Model (PTM), simultaneously learns the student’s coding skills based on their coding history and predicts whether they will struggle on a new task. We extensively evaluated the effectiveness of the PTM model on two separate datasets from introductory Java and Python courses for beginner programmers. Experimental results demonstrate that PTM outperforms state-of-the-art models in predicting struggling students. The paper showcases the potential of combining structured insights from teachers for early identification of those needing assistance in learning to code.

Details

ECAI Conference 2023 Conference Paper

Online Evaluation of Tail Project Boosting in Citizen Science

Amit Sultan
Avi Segal
Guy Shani
Darlene Cavalier
Kobi Gal

In citizen science, regular people provide invaluable information by contributing to scientific projects. Citizen science platforms, such as SciStarter, provide easy access to numerous such projects. Often, users contribute mainly to a relatively small set of popular projects, while it is difficult for many projects to draw the attention of users. Thus, increasing the contribution of users to such low-popularity projects may increase scientific and societal impact. In this paper, we explore the power of a recommender system to draw attention to less popular projects. Standard use of recommendation systems often leads to limited exposure of less popular (tail) projects. We thus propose a re-ranking approach based on “lift boosting, ” which uses the statistical lift measure to enhance the exposure of tail projects. By combining lift and traditional relevance measures, our method re-ranks the recommendation list to emphasize projects that are both relevant to the user while also have a high lift value. We implement our approach on SciStarter, one of the biggest citizen science platforms on the web. We conduct an online experiment involving over 2000 real users. Our results show a positive shift towards less popular projects without compromising overall contribution rates. This work demonstrates the potential of our lift-boosting method for promoting the discovery of tail projects in citizen science platforms, thereby fostering a more diverse range of scientific contributions.

Details

AAAI Conference 2021 Conference Paper

Intelligent Recommendations for Citizen Science

Daniel Ben Zaken
Kobi Gal
Guy Shani
Avi Segal
Darlene Cavalier

Citizen science refers to scientKobiific research that is carried out by volunteers, often in collaboration with professional scientists. The spread of the internet has allowed volunteers to contribute to citizen science projects in dramatically new ways while creating scientific value and gaining pedagogical and social benefits. Given the sheer size of available projects, finding the right project, which best suits the user preferences and capabilities, has become a major challenge and is essential for keeping volunteers motivated and active contributors. We address this challenge by developing a system for personalizing project recommendations which was fully deployed in the wild. We adapted several recommendation algorithms to the citizen science domain from the literature based on memory-based and model-based collaborative filtering approaches. The algorithms were trained on historical data of users’ interactions in the SciStarter platform - a leading citizen science site - as well as their contributions to different projects. The trained algorithms were evaluated in SciStarter and involved hundreds of users who were provided with personalized recommendations for new projects they had not contributed to before. The results show that using the new recommendation system led people to increased participation in new SciStarter projects when compared to groups that were recommended projects using nonpersonalized recommendation approaches, and compared to behavior before recommendations. In particular, the group of volunteers receiving recommendations created by an SVD algorithm (matrix factorization) exhibited the highest levels of contributions to new projects, when compared to the other cohorts. A follow-up survey conducted with the SciStarter community confirmed that users felt that the recommendations matched their personal interests and goals. Based on these results, our recommendation system is now fully integrated into the SciStarter portal, positively affecting hundreds of users each week, and leading to social and educational benefits.

PDF Details

AAAI Conference 2018 Conference Paper

Optimizing Interventions via Offline Policy Evaluation: Studies in Citizen Science

Avi Segal
Kobi Gal
Ece Kamar
Eric Horvitz
Grant Miller

Volunteers who help with online crowdsourcing such as citizen science tasks typically make only a few contributions before exiting. We propose a computational approach for increasing users’ engagement in such settings that is based on optimizing policies for displaying motivational messages to users. The approach, which we refer to as Trajectory Corrected Intervention (TCI), reasons about the tradeoff between the long-term inﬂuence of engagement messages on participants’ contributions and the potential risk of disrupting their current work. We combine model-based reinforcement learning with off-line policy evaluation to generate intervention policies, without relying on a ﬁxed representation of the domain. TCI works iteratively to learn the best representation from a set of random intervention trials and to generate candidate intervention policies. It is able to reﬁne selected policies off-line by exploiting the fact that users can only be interrupted once per session. We implemented TCI in the wild with Galaxy Zoo, one of the largest citizen science platforms on the web. We found that TCI was able to outperform the state-of-the-art intervention policy for this domain, and signiﬁcantly increased the contributions of thousands of users. This work demonstrates the beneﬁt of combining traditional AI planning with off-line policy methods to generate intelligent intervention strategies.

PDF Details

IJCAI Conference 2016 Conference Paper

Intervention Strategies for Increasing Engagement in Crowdsourcing: Platform, Predictions, and Experiments

Avi Segal
Ya'akov (Kobi) Gal
Ece Kamar
Eric Horvitz
Alex Bowyer
Grant Miller

Volunteer-based crowdsourcing depend critically on maintaining the engagement of participants. We explore a methodology for extending engagement in citizen science by combining machine learning with intervention design. We first present a platform for using real-time predictions about forthcoming disengagement to guide interventions. Then we discuss a set of experiments with delivering different messages to users based on the proximity to the predicted time of disengagement. The messages address motivational factors that were found in prior studies to influence users' engagements. We evaluate this approach on Galaxy Zoo, one of the largest citizen science application on the web, where we traced the behavior and contributions of thousands of users who received intervention messages over a period of a few months. We found sensitivity of the amount of user contributions to both the timing and nature of the message. Specifically, we found that a message emphasizing the helpfulness of individual users significantly increased users' contributions when delivered ac- cording to predicted times of disengagement, but not when delivered at random times. The influence of the message on users' contributions was more pronounced as additional user data was collected and made available to the classifier.

PDF Details