arXiv subjects

S. R. S. Iyengar

Publications and source records attributed to S. R. S. Iyengar.

At least 19 recordsLinked to original sources

arXiv2026

Scaling Peer Assessments: An Integrity Report from a Large Engineering Internship

Assessing learning in large classrooms presents a significant challenge for individual instructors, who may have limited capacity to evaluate the understanding, participation, and assessment behaviour of every student. Peer assessments have been a way of distributing this responsibility among learners, allowing them to evaluate and provide feedback to one another while reducing dependence on instructor-led assessments. Building on this approach, we implemented a peer validation model within a large, multi-institutional internship programme in which students who demonstrated sufficient understanding were authorised to assess and validate their peers through short oral discussions. The assessment process began with the instructor validating a small group of students, who were then authorised to validate their peers, allowing the process to gradually expand across the cohort and operate at scale. This study examines how participants experienced the model and the extent to which assessment integrity was maintained, using an end-of-programme survey of 238 consenting respondents. Most participants regarded the activity as worthwhile, with 79.8% reporting that they solved problems they could not previously solve. However, 29.0% acknowledged at least one instance of reduced effort, a lowered validation standard, or reciprocal validation, while 88.7% believed that at least a little validation had occurred without proper examination. When asked how the process could be strengthened, participants selected post-validation discussion of solutions approximately twice as often as closer auditing or mentor-led validation. These findings provide descriptive evidence of both the potential and the integrity challenges of using peer validation as a scalable assessment approach in large learning environments.

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arXiv2026

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

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.

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arXiv2026

Measuring Student Self-Assessment against Viva-Demonstrated Mastery in a Large First-Year Programming Course

Mastery-based education increasingly places the reporting of learning progress in students' hands, who record task completion on learning dashboards. The usefulness of such self-reports depends on how closely reported mastery corresponds to demonstrated competence. Most evidence on student self-assessment compares an overall self-rating with an overall examination score and therefore provides limited evidence about which tasks or which students account for the mismatch. This study examines first-year students' self-assessment against viva-demonstrated mastery at the level of individual tasks across a ladder of sixty programming tasks. The study draws on a large first-year programming course taught in 2023, involving 203 students and 12 examiners, in which every reported task was verified through an oral viva. Because a task entered the Viva only after it was reported, the design is one-sided and captures over-estimation but not under-estimation. Of 11,093 reported tasks, 10,885 (98.1%) were demonstrated, indicating a high degree of correspondence between self-report and demonstrated mastery. The remaining 208 overestimations were not evenly distributed. A small number of students accounted for most of the errors, and they occurred mainly on difficult tasks near the end of the task ladder rather than on higher-point tasks. This task-level analysis shows that high overall self-assessment accuracy can coexist with specific areas where reported and demonstrated mastery diverge. It also provides a practical basis for directing additional verification and formative feedback toward students and tasks where such divergence is more likely.

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arXiv2026

What Students Actually Ask: Demand Structure and Automation Potential in a Hybrid Support System

Large online programmes receive heavy volumes of queries during onboarding, at a scale that grows faster than the number of staff available to answer them. This paper reports a study of an AI integrated query resolution platform that spreads incoming queries across four routes: an AI based assistant, answers from fellow participants, a curated corpus of frequently asked questions, and escalation to administrators. Over nine weeks, from 2 May to 6 July 2026, the system handled 4,093 queries raised by 1,434 participants. Nearly every query reached a recorded resolution, and one query in five closed within an hour. Reuse of 114 corpus entries absorbed 21.3% of the volume, participants resolved a further 12.2% on their own, and 132 participants answered questions for one another at a median of 9 to 15 minutes, showing that peer answering, where it occurred, was fast and broadly shared across the cohort. Classifying the query text shows that demand was narrow rather than varied. A single process step, the submission of a certificate and the offer letter that follows it, accounts for 56.8% of corpus mediated resolutions, and at least 20.4% of queries concern the progress of a pending submission rather than a request for information, a class the assistant served only 1.3% of the time, since a stored answer cannot report an individual's current status. Only 8.0% of the queries handled by a person duplicated content already in the corpus, which indicates that the knowledge base was already well used. Most direct administrative closures occur in synchronous bulk events, a pattern that shapes how the records should be read.

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arXiv2026

When Attention Guardrails Become Barriers to Learning: Towards the Tipping Point

Online learning offers flexibility but lacks the structure of a classroom, where a teacher's presence guides attention. The platform we study restores that structure by monitoring the learner through the webcam during ordinary coursework, interrupting or restarting a video when the learner appears distracted. What such monitoring does to a learner across a whole course, rather than in a single examination, is largely unexamined. We report a convergent mixed-methods study of one monitored course pipeline in a summer internship program. We read two free-text surveys alongside three platform channels. The dropout exit survey yielded 15 analyzable responses; the persisting-learner reflection survey, 36. The channels are camera-verification telemetry (14,529 flags from 448 students), an in-video emotion widget (615 submissions from 273 students), and a mandatory end-of-course survey (up to 634 respondents per item). Neither survey named monitoring, so every mention analyzed here was raised by the respondent. Focus-monitoring was raised by 18 of the 51 free-text respondents: 7 of 15 dropouts and 11 of 36 persisting learners. Among the dropouts who raised it, focus-monitoring was the stated primary cause of departure in 4 of 7 cases. None of the 18 questioned being observed in principle. What learners contest is the misreading of ordinary actions, drinking water or moving the head, and the severity of what follows a flag: a video already watched returns to the start of its segment. These findings identify two targets for redesign: the severity of the response to a flag, and the environment check, which can flag a learner before any content has been seen. We argue that the proportionality of that response marks the point at which an attention guardrail becomes a barrier to learning, the tipping point this study approaches.

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arXiv2026

Engagement-Led Segmentation of Gamified Participation Data in a Large-Scale Remote Internship: A Mixed-Methods Study

Gamified points are widely used to represent learner participation, but cumulative point totals provide limited information about how participation changes over time. This limitation matters in continuously enrolling programmes, where learners have different opportunities to accumulate points. This study examines how gamified participation data can be interpreted as indicators of behavioural engagement in a large-scale, continuously enrolling remote internship. Using an anonymised operational dataset, 3,607 distinct started learners were first classified by participation status, separating dormant learners from those with observable participation. The 1,871 active learners were then grouped using K-means clustering on normalised longitudinal and opportunity-aware participation features. Four participation patterns emerged: Thriving Core, Tapering, Fast Starters, and Occasional Participants. Their trajectories differed in both level and direction. A perception survey of 597 respondents provided complementary learner-reported evidence, integrated with the behavioural strand through a joint display by segment. Perceptions differed across segments, while individual-level correlations between perceptions and behaviour were small, and learners with low recorded participation could report positive perceptions of the points system alongside external barriers. Segments assigned from the first four weeks were then checked against eleven weeks of later platform records: 94% of the Thriving Core attended at least ten further sessions and 22% completed the internship, against under 7% and under 1% in the low-engagement segments. The findings suggest that gamified participation data are more informative when interpreted as longitudinal, opportunity-aware behavioural indicators rather than cumulative scores alone.

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arXiv2026

From Early Participation to Later Completion: Evidence from a Large-Scale Self-Paced Learning Programme

Large-scale learning programmes generate records that make learner participation observable across different activities. Participation points are commonly used to record and encourage such participation, but their value may extend beyond the activities for which points are awarded. Existing evaluations often examine gamification outcomes within the activities or learning environments in which the game elements are implemented, providing limited evidence about whether early participation points contain information about later participation outside the points system. This study examines whether early participation points can provide information about learners' later participation in a self-paced learning track that does not award participation points. Using anonymised records from 876 learners in a large-scale remote software-upskilling internship, we examined participation points generated from live-session attendance and poll responses against later self-paced course completion. The primary analysis used the 438 learners who earned at least one point during the first week, while the full cohort was retained for the no-point analysis. Week-one participation points distinguished learners who later completed a self-paced course with an AUC of 0.89, increasing to 0.95 by the fourth week. Similar AUCs were observed at both stages of the self-paced course sequence, while the absence of week-one points identified learners who did not start or did not complete a self-paced course with 95% precision. These findings indicate that early participation points can provide information about later participation outside the activities that generate the points. Such information can help large-scale learning programmes identify learners who may require timely attention while learning is still in progress, without treating participation points as a measure of overall learner engagement.

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arXiv2022

A Multi-Opinion Based Method for Quantifying Polarization on Social Networks

Social media platforms have emerged as a hub for political and social interactions, and analyzing the polarization of opinions has been gaining attention. In this study, we have proposed a measure to quantify polarization on social networks. The proposed metric, unlike state-of-the-art methods, does not assume a two-opinion case and applies to multiple opinions. We tested our metric on different networks with a multi-opinion scenario and varying degrees of polarization. The scores obtained from the proposed metric were comparable to state-of-the-art methods on binary opinion-based benchmark networks. The technique also differentiated among networks with different levels of polarization in a multi-opinion scenario. We also quantified polarization in a retweet network obtained from Twitter regarding the usage of drugs like hydroxychloroquine or chloroquine in treating COVID-19. Our metric indicated a high level of polarized opinions among the users. These findings suggest uncertainty among users in the benefits of using hydroxychloroquine and chloroquine drugs to treat COVID-19 patients.

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arXiv2022

A Bi-level assessment of Twitter in predicting the results of an election: Delhi Assembly Elections 2020

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to the tweets of candidates are good indicators for predicting actual election outcome. However, we observe that the number of tweets mentioning a party and the sentiment of voters towards the party shown in tweets are not aligned with the election result. We also use machine learning models on various features such as linguistic, word embeddings and moral dimensions for predicting the election result (win or lose). The random forest model using tweet features provides promising results for predicting if the tweet belongs to a winning or losing candidate.

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arXiv2021

Morality-based Assertion and Homophily on Social Media: A Cultural Comparison between English and Japanese Languages

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users.

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arXiv2019

Investigating Ortega Hypothesis in Q&A portals: An Analysis of StackOverflow

Ortega Hypothesis considers masses, i.e., a large number of average people who are not specially qualified as being instrumental in any system's progress. This hypothesis has been reasonably examined in the scientific domain where it has been supported by a few works while refuted by many others, resulting in no clear consensus. While the hypothesis has only been explored in the scientific domain so far, it has hardly been examined in other fields. Given the large-scale collaboration facilitated by the modern Q&A portals where a crowd with a diverse skill-set contributes, an investigation of this hypothesis becomes necessary for informed policy-making. In this work, we investigate the research question inspired by Ortega Hypothesis in StackOverflow where we examine the contribution made by masses and check whether the system may continue to function well even in their absence. The results point towards the importance of masses in Q&A portals for the little but useful contribution that they provide. The insights obtained from the study may help in devising informed incentivization policies enabling better utilization of the potential of the users.

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arXiv2019

Estimating the Degree Centrality Ranking of a Node

Complex networks have gained more attention from the last few years. The size of real-world complex networks, such as online social networks, WWW network, collaboration networks, is increasing exponentially with time. It is not feasible to collect the complete data and store and process it. In the present work, we propose a method to estimate the degree centrality rank of a node without having the complete structure of the graph. The proposed algorithm uses the degree of a node and power-law exponent of the degree distribution to calculate the ranking. Simulation results on the Barabasi-Albert networks show that the average error in the estimated ranking is approximately $5\%$ of the total number of nodes.

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arXiv2018

Social Network Analysis of the Caste-Based Reservation System in India

It has been argued that the reservation system in India, which has existed since the time of Indian Independence (1947), has caused more havoc and degradation than progress. This being a popular public opinion, has not been based on any rigorous scientific study or research. In this paper, we revisit the cultural divide among the Indian population from a purely social network based approach. We study the distinct cluster formation that takes place in the Indian community and find that this is largely due to the effect of caste-based homophily. To study the impact of the reservation system, we define a new parameter called social distance that represents the social capital associated with each individual in the backward class. We study the changes that take place with regard to the average social distance of a cluster when a new link is established between the clusters which in its essence, is what the reservation system is accomplishing. Our extensive study calls for the change in the mindset of people in India. Although the animosity towards the reservation system could be rooted due to historical influence, hero worship and herd mentality, our results make it clear that the system has had a considerable impact on the country's overall development by bridging the gap between the conflicting social groups. The results also have been verified using the survey and are discussed in the paper.

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arXiv2018

Estimating Shell-Index in a Graph with Local Information

For network scientists, it has always been an interesting problem to identify the influential nodes in a given network. The k-shell decomposition method is a widely used method which assigns a shell-index value to each node based on its influential power. The k-shell method requires the global information of the network to compute the shell-index of a node that is infeasible for large-scale real-world dynamic networks. In this work, we propose a method to estimate the shell-index of a node using its local information. We also propose hill-climbing based approach to hit the top-ranked nodes in a small number of steps. We further discuss a method to estimate the rank of a node based on the proposed estimator.

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arXiv2017

Global Rank Estimation

In real world complex networks, the importance of a node depends on two important parameters: 1. characteristics of the node, and 2. the context of the given application. The current literature contains several centrality measures that have been defined to measure the importance of a node based on the given application requirements. These centrality measures assign a centrality value to each node that denotes its importance index. But in real life applications, we are more interested in the relative importance of the node that can be measured using its centrality rank based on the given centrality measure. To compute the centrality rank of a node, we need to compute the centrality value of all the nodes and compare them to get the rank. This process requires the entire network. So, it is not feasible for real-life applications due to the large size and dynamic nature of real world networks. In the present project, we aim to propose fast and efficient methods to estimate the global centrality rank of a node without computing the centrality value of all nodes. These methods can be further extended to estimate the rank without having the entire network. The proposed methods use the structural behavior of centrality measures, sampling techniques, or the machine learning models. In this work, we also discuss how to apply these methods for degree and closeness centrality rank estimation.

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arXiv2017

Degree Ranking Using Local Information

Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only $1\%$ samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for random networks and validate their efficiency on synthetic random networks, that are generated using Erdős-Rényi model. Results show that the proposed methods can be efficiently used for random networks as well.

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arXiv2017

A Faster Method to Estimate Closeness Centrality Ranking

Closeness centrality is one way of measuring how central a node is in the given network. The closeness centrality measure assigns a centrality value to each node based on its accessibility to the whole network. In real life applications, we are mainly interested in ranking nodes based on their centrality values. The classical method to compute the rank of a node first computes the closeness centrality of all nodes and then compares them to get its rank. Its time complexity is $O(n \cdot m + n)$, where $n$ represents total number of nodes, and $m$ represents total number of edges in the network. In the present work, we propose a heuristic method to fast estimate the closeness rank of a node in $O(α\cdot m)$ time complexity, where $α= 3$. We also propose an extended improved method using uniform sampling technique. This method better estimates the rank and it has the time complexity $O(α\cdot m)$, where $α\approx 10-100$. This is an excellent improvement over the classical centrality ranking method. The efficiency of the proposed methods is verified on real world scale-free social networks using absolute and weighted error functions.

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arXiv2017

How Does Knowledge Come By?

Although the amount of knowledge that the humans possess has been gradually increasing, we still do not know the procedure and conditions that lead to the creation of new knowledge. An understanding of the modus operandi for the creation of knowledge may help in accelerating the existing pace of building knowledge. Our state of ignorance regarding various aspects of the process of knowledge building is highlighted by the existing literature in the domain. The reason behind it has been our inability to acquire the underlying data of this complex process. However, current time shows great promise of improvements in the knowledge building domain due to the availability of several online knowledge building portals. In this report, we emphasise that these portals act as prototypes for universal knowledge building process. The analysis of big data availed from these portals may equip the knowledge building researchers with the much needed meta-knowledge.

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