arXiv subjects

Kshitij Sharma

Publications and source records attributed to Kshitij Sharma.

8 recordsLinked to original sources

arXiv2026

Can providing feedback on gaze and mental-effort synchrony improve pair programming performance?

Pair programming is a widely used collaborative learning practice in computer science education yet its effectiveness varies substantially due to breakdowns in coordination attention and cognitive regulation between partners. This paper investigates whether AI supported feedback grounded in joint visual attention and joint mental effort can improve collaborative programming performance and how feedback timing shapes learner AI interaction. Two experimental studies using dual eye tracking capture real time indicators of collaborative regulation during debugging tasks. Study 1 examines reactive feedback that intervenes when observed joint visual attention or joint mental effort deviates beyond predefined thresholds while Study 2 evaluates proactive feedback that forecasts future regulatory breakdowns using machine learning models and intervenes pre emptively. Across both studies feedback effectiveness is assessed through debugging success time on task and feedback uptake reflected in code changes. Multimodal feedback significantly improves collaborative performance compared to no feedback conditions. Reactive feedback yields strong gains in debugging success and efficiency particularly when joint visual attention and joint mental effort based feedback are combined. Proactive forecast based feedback further enhances performance reduces time on task and increases constructive feedback uptake while relying less on intrusive interventions. Proactive feedback better preserves learner agency by maintaining optimal collaboration states, particularly for high-performing pairs. These findings demonstrate that gaze and mental effort synchrony can serve as reliable actionable triggers for AI supported collaborative learning highlighting the importance of feedback timing transparency and anticipatory regulation in supporting effective pair programming.

cs.HC↗

arXiv2026

Cognitive Alignment Drives Attention: Modeling and Supporting Socially Shared Regulation in Pair Programming

Grounded in socially shared regulation of learning (SSRL), this paper investigates how joint mental effort (JME) and joint visual attention (JVA) serve as process-level indicators of shared regulation in pair programming and how AI-driven adaptive feedback can strengthen these processes. We present three eye-tracking studies involving 182 dyads engaged in collaborative debugging tasks. Study 1 examines natural collaboration and shows that high-performing dyads exhibit significantly higher JME and JVA, a greater prevalence of productive high-JME-high-JVA episodes, and a stable causal relationship in which JME predicts JVA. Study 2 evaluates reactive adaptive feedback based on real-time deviations in JME and/or JVA. Results show that combined feedback targeting both dimensions yields the strongest improvements in performance, regulatory coherence, and cognitive-to-attentional causality, outperforming single-channel feedback. Study 3 introduces proactive, forecast-based feedback using machine-learning predictions of future collaboration states. Proactive support further enhances performance and sustains shared regulation by anticipating breakdowns before they manifest. Across studies, causal modeling reveals that cognitive alignment systematically drives attentional coordination in successful collaboration, while mismatches between effort and attention characterize unproductive regulation. Methodologically, this work integrates dual eye-tracking, pupillometry, episode-based analysis, and causal inference to capture SSRL as a dynamic, emergent process. Conceptually, the findings position AI not as an automated controller, but as an intelligence-augmenting co-regulator that supports learners' capacity to coordinate effort, attention, and understanding together.

cs.HC↗

arXiv2026

RTMS: A Real-Time Multimodal Scaffolding System for Improving Debugging in Computing Education

Debugging is a demanding aspect of programming yet guidance on how to teach it effectively remains limited. Novices often struggle to recognize impasses regulate their problem solving and manage cognitive load and stress. This study investigates whether real time multimodal feedback triggered by indicators of cognitive load and physiological stress can improve debugging performance narrow expert novice gaps and reduce the influence of prior programming experience on success. We conducted a between subjects experiment with 120 undergraduate computer science students who debugged a medium sized Python program. Participants were assigned to one of four conditions no feedback cognitive load triggered feedback stress triggered feedback or combined trigger feedback. Eye tracking and heart rate variability data were used to detect moments of struggle and automatically deliver brief context sensitive hints. All three feedback conditions significantly improved debugging success and efficiency compared with the control group. Cognitive load triggered feedback produced stronger gains than stress triggered feedback and the combined trigger condition yielded the largest improvements. Programming expertise predicted performance only in the control condition and in all feedback conditions the novice expert gap was markedly reduced. Adaptive feedback that responds to learners cognitive and affective states can help manage debugging demands and reduce performance differences linked to prior experience highlighting opportunities for physiologically aware adaptive learning environments.

cs.HC↗

arXiv2026

Not All Scaffolds Are Equal: How Initiation Mode Determines EMME Effectiveness in Debugging

Adaptive learning technologies increasingly rely on real time physiological analytics to trigger instructional support automatically yet how system driven decisions interact with learners ongoing problem solving processes remains poorly understood. Eye Movement Modeling Examples have shown promise as attention guidance tools but have been studied predominantly as static instructional materials rather than as adaptive scaffolds whose timing and initiation control can vary. This study investigates whether scaffold initiation mode shapes EMME effectiveness in novice programmers debugging and specifically whether automated triggering based on a single physiological indicator of low mental effort is a viable basis for adaptive scaffold delivery. A between subjects experiment was conducted with 120 undergraduate computer science students randomly assigned to one of four conditions: teacher initiated, learner initiated, automated or no scaffold control. Participants completed ten Python debugging tasks while eye tracking data, video interaction logs and performance scores were recorded. All EMME conditions outperformed the control. However human mediated initiation whether teacher or learner consistently produced higher performance than automated triggering and more integrative engagement with the EMME material. Automated triggering based on sustained low pupillary activity was associated with disruptive behavioral patterns suggesting mistimed delivery. EMME also eliminated the performance advantage of prior programming knowledge across all initiation modes. These findings establish scaffold initiation timing and control as critical design variables for EMME and adaptive learning technologies more broadly and demonstrate that a single low effort physiological threshold is insufficient as a trigger criterion for complex problem solving support.

cs.HC↗

arXiv2026

ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming

Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems remain individual-centric and reactive. This paper introduces ProPACT, a proactive AI-driven adaptive collaborative tutor that treats collaboration itself as the object of instruction. ProPACT constructs a multimodal dyadic learner model based on Joint Visual Attention (JVA), Joint Mental Effort (JME), and individual mental effort, and employs an XGBoost-based forecasting model to predict emerging suboptimal collaboration states up to 30 seconds in advance. These predictions drive a hierarchical adaptive policy that delivers minimally intrusive scaffolds while fading support during productive collaboration. A within-subject study with 26 pair-programming dyads shows that proactive feedback significantly improves debugging success, task efficiency, feedback uptake, and post-intervention gains in JVA and JME, demonstrating the potential of forecast-driven dyadic adaptivity for real-time collaborative learning regulation.

cs.HC↗

arXiv2024

Bulk-like structural, magnetic and optical properties of (111)- and (001)-NiO thin films

We have grown (111)- and (001)-oriented NiO thin films on (0001)-Sapphire and (001)-MgO substrates using pulsed laser deposition (PLD), respectively. DC magnetic susceptibility measurements underline that the N\'eel temperatures of the samples are beyond room-temperature. This is further confirmed by the presence of two-magnon Raman scattering modes in these films in ambient conditions. Moreover, relative intensity of the two magnon-mode with respect to a neighboring phonon mode in the films, at least down to 30 nm thickness, is comparable to the same for bulk NiO. UV-vis spectroscopy and spectroscopic ellipsometry determined that the bandgap of the films is 3.6 eV which is well within the range for bulk NiO. Thus, these indicate that the thin films are bulk-like. Further, photoluminescence measurements on (111)-NiO films obtained two-radiative transitions at 385 and 405 nm. The linewidth of the latter broadens towards low temperatures, indicating a plausible exciton-magnon coupling. Overall, these PLD-grown oxide films hold significant technological importance due to their optical transparency and their capacity to host robust magnons at room temperature.

cond-mat.mtrl-sci↗

arXiv2024

Mental health of computing professionals and students: A systematic literature review

The intersections of mental health and computing education is under-examined. In this systematic literature review, we evaluate the state-of-the-art of research in mental health and well-being interventions, assessments, and concerns like anxiety and depression in computer science and computing education. The studies evaluated occurred across the computing education pipeline from introductory to PhD courses and found some commonalities contributing to high reporting of anxiety and depression in those studied. In addition, interventions that were designed to address mental health topics often revolved around self-guidance. Based on our review of the literature, we recommend increasing sample sizes and focusing on the design and development of tools and interventions specifically designed for computing professionals and students.

cs.CY↗

arXiv2024

Quantum chaos in PT symmetric quantum systems

In this study, we explore the interplay between $\mathcal{PT}$-symmetry and quantum chaos in a non-Hermitian dynamical system. We consider an extension of the standard diagnostics of quantum chaos, namely the complex level spacing ratio and out-of-time-ordered correlators (OTOCs), to study the $\mathcal{PT}$-symmetric quantum kicked rotor model. The kicked rotor has long been regarded as a paradigmatic dynamic system to study classical and quantum chaos. By introducing non-Hermiticity in the quantum kicked rotor, we uncover new phases and transitions that are absent in the Hermitian system. From the study of the complex level spacing ratio, we locate three regimes -- one which is integrable and $\mathcal{PT}$-symmetry, another which is chaotic with $\mathcal{PT}$-symmetry and a third which is chaotic but with broken $\mathcal{PT}$-symmetry. We find that the complex level spacing ratio can distinguish between all three phases. Since calculations of the OTOC can be related to those of the classical Lyapunov exponent in the semi-classical limit, we investigate its nature in these regimes and at the phase boundaries. In the phases with $\mathcal{PT}$-symmetry, the OTOC exhibits behaviour akin to what is observed in the Hermitian system in both the integrable and chaotic regimes. Moreover, in the $\mathcal{PT}$-symmetry broken phase, the OTOC demonstrates additional exponential growth stemming from the complex nature of the eigenvalue spectrum at later times. We derive the analytical form of the late-time behaviour of the OTOC. By defining a normalized OTOC to mitigate the effects caused by $\mathcal{PT}$-symmetry breaking, we show that the OTOC exhibits singular behaviour at the transition from the $\mathcal{PT}$-symmetric chaotic phase to the $\mathcal{PT}$-symmetry broken, chaotic phase.

quant-ph↗