Search arXiv⌕ Search

arXiv · 2610.02595

Effects of a Behavioural Commitment Scheme on Study Regularity in a Self-Paced Learning Platform

Abstract

Online education has enabled learners worldwide to take up courses from reputed institutions. However, a self-paced online course cannot guarantee the motivation and engagement that a learner experiences in a real-time classroom. Self-paced access also makes platform load unpredictable, which drives up the compute cost. We propose a Commitment Scheme for course access that aligns learner commitment with platform capacity. Learners book their study slots in advance; the instructor sets the budget of learning hours available for the course; and learners who use a full window earn additional watch hours. A booked window records an intention to study at a stated time, and a learner who appears in that window implements it. The byproduct is a platform load that can be forecast and bounded. The study involved two courses taken in sequence by the same learners, with the slot booking system activated only in the second. In-window study was observed on 86.8% of booked windows, and the median committer placed 95.5% of all study time inside self-booked windows. Among learners who studied across the launch, study regularity improved from 1.01 to 1.33 active days per week, with a supporting difference of +1.37 days per week against the same learners' preceding course. Commitments made on the same day as the study slot were honoured more often than advance bookings (88.8% against 62.5% two days ahead), which is consistent with the classic intention-behaviour gap.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Meenakshi V., Pavani Ayinampudi, Aditya B. M. V., Jinal Gupta, Prakash Hegade, Rohit Sharma, Sakshi Sharma, S. R. S. Iyengar. 2026-10-01. Effects of a Behavioural Commitment Scheme on Study Regularity in a Self-Paced Learning Platform. https://arxiv.org/abs/2610.02595

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

VIRENA: Virtual Arena for Research, Education, and Democratic Innovation

Digital platforms shape how people communicate, deliberate, and form opinions. Studying these dynamics has become harder because of restricted data access, ethical limits on real-world experiments, and the technical demands of existing research tools. VIRENA (Virtual Arena) is a platform for controlled experiments in realistic social media environments. Several participants can interact at the same time in replicas of feed-based platforms (Instagram, Facebook, Reddit, X) and messaging apps (WhatsApp, Messenger). AI agents powered by large language models (LLMs) join the participants with configurable personas and human-like timing. Researchers set up experiments in a visual interface without programming: they define conditions, schedule stimulus content, add moderation rules, assign participants randomly to conditions, and export the data. VIRENA supports designs that were hard to run before, such as studying human--AI interaction in realistic social settings, comparing moderation interventions, and observing group deliberation as it unfolds. Participants and data stay within institutional control, and the platform links to survey and recruitment tools. This paper describes how VIRENA works and how to use it.

cs.HC↗

DimSteer: Steering LLM Authoring with Automatically Discovered Stylistic Controls

Large language model writing interfaces often make users steer outputs by repeatedly articulating desired changes in natural language. Yet writers may recognize useful stylistic directions only after seeing alternatives, making revision recall-heavy. We present DimSteer, an authoring interface that samples prompt-local completions, discovers high-variance activation-space axes of variation, labels them, and exposes them as sliders with pole previews, diff comparison, and reset controls. Users can manipulate discovered dimensions, reducing the need to reformulate prompts for each stylistic adjustment. In a within-subjects study with 16 participants against a matched prompt-only baseline, DimSteer reduced mental demand, effort, and frustration while preserving comparable perceived success. Participants valued the surfaced dimensions, yet 15 of 16 disagreed that they would have thought to request the same changes in a prompt. Results suggest prompt-local controls can shift LLM authoring from recall-based prompting toward recognition-based exploration and direct manipulation, while preserving prompting for open-ended edits.

cs.HC↗

Evaluating Just Noticeable Differences in Layered Opacity Visualizations

Opacity is a widely used channel in data visualization, but it remains less well understood compared to channels such as color, length, size, etc. Recent work from Meng et al. investigated the impact of opacity across competing color schemes, finding that certain color schemes were associated with better participant accuracy. We examine these effects further in a controlled two-alternative forced-choice setup to determine whether opacity differences are truly equal across possible opacity comparison ranges. In a within-subjects study with 96 trials, including two competing color schemes (best and worst from Meng et al.) and 48 opacity pairs, we find little differences between color schemes but larger individual differences in accuracy. Further, results show stable performance in middle opacity ranges, with more errors occurring when comparing extreme values. We discuss potential implications for design guidelines and further study and make our study materials, analysis scripts, and data available at https://osf.io/zv9dx/overview?view_only=38d03cea1b3d42788593e3e6b1016cfd.

cs.HC↗