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

Sakshi Sharma, Pavani Ayinampudi, Aditya B. M. V., Jinal Gupta, Prakash Hegade, Rohit Sharma, Meenakshi V, S. R. S. Iyengar

arXiv:2609.39750v1cs.HCcs.CY

TL;DR

Cumulative point totals do not show how participation changes over time, especially when learners in a continuously enrolling programme have different opportunities to earn points. The study combines opportunity-aware longitudinal segmentation with learner perceptions and later platform records; early segments distinguish subsequent participation patterns, but validate persistence rather than learning.

  • Problem

    Cumulative point totals conceal changes in participation over time, while continuous enrolment gives learners different opportunities to accumulate points.

    Source: §1 · p. 2

  • Method

    The study separates learners without recorded participation from active learners, clusters active learners using standardised longitudinal features, and integrates platform patterns with survey perceptions and later records.

    Source: §3.2 · pp. 4, 6, §5.2 · p. 14

  • Results

    Early segments separated later persistence, with later milestones concentrated in the Thriving Core; segment-level perception differences coexisted with small individual-level perception-behaviour correlations.

    Source: §5.2 · pp. 14–15

  • Takeaways & Limitations

    Gamified participation data are more informative as longitudinal, opportunity-aware behavioural indicators than as cumulative scores alone, while learner reports can add context to low recorded participation.

    Source: §5.2 · p. 15, §6 · p. 16

    The observational study makes no causal claims, and no learning outcome measured outside the platform was available.

    Source: §5.3 · p. 15

Abstract

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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.

1 Introduction

Cumulative point totals hide how participation changes over time, a limitation amplified when continuously enrolling learners have unequal opportunities to earn points. The study examines longitudinal participation patterns alongside learner perceptions and later platform outcomes.

1 Introduction

  • Point totals can conceal contrasting participation trajectories, making cumulative scores alone insufficient to describe how participation changes over time.Learners with equal totals may participate consistently, become inactive after an active start, or increase participation gradually.Source: §1 · p. 2
  • In continuously enrolling programmes, learners join at different times and have unequal opportunities to accumulate points.Remote internships rely on digital traces such as attendance, meeting participation, and poll responses to monitor participation.Source: §1 · p. 2
  • The study examines participation trajectories in routinely collected gamification data and interprets points as behavioural indicators rather than direct measures of engagement.Points capture observable activities, not cognitive or emotional involvement.Source: §1 · p. 2
  • The study reads participation trajectories alongside learner perceptions, using first-four-week indicators and later platform records to examine subsequent participation and completion.The stated aim is to improve interpretation of points for monitoring engagement in continuously enrolling internship programmes.Source: §1 · p. 3

2 Background Study

Remote learning traces can make behavioural participation visible, but they do not capture all dimensions of engagement or explain learners’ reported experiences. Prior work on gamification and trajectory analysis motivates a longitudinal, learner-reported perspective.

2 Background Study

  • Engagement includes behavioural, emotional, and cognitive dimensions, while platform records most directly expose behavioural traces such as attendance and poll responses.In this study, those records indicate participation, not motivation or learning.Source: §2 · p. 3
  • Gamification effects are mixed and context-dependent, and points jointly reflect rule design, opportunity, and behaviour.Points can reward surface participation and appear unfair when opportunities to earn are unequal.Source: §2 · p. 3
  • Trajectory research finds distinct learner patterns, but gamification points have received less longitudinal attention in continuously enrolling programmes with unequal earning opportunities.Learner reports add information about perceptions and reasons that behavioural traces alone cannot explain.Source: §2 · pp. 3–4

3 Methodology and Methods

The study asks how opportunity-aware participation data can indicate behavioural engagement, how perceptions relate to segments, and whether early segments distinguish later outcomes. It combines platform records and survey evidence through a two-tier segmentation design.

3.1 Research Questions

  • The research questions address trajectory patterns, alignment and divergence between perceptions and behaviour, reported barriers, and prediction of outcomes over the eleven weeks after segmentation.The outcome question asks whether trajectory shape adds information beyond points earned in the same period.Source: §3.1 · p. 4

3.2 Research Design and the Two-Tier Model

  • A convergent mixed-methods design combines observational platform records with a learner perception survey, integrating them by segment in a joint display.Behavioural records describe what learners did; survey responses provide perceptions, while the analysis remains descriptive and makes no causal claims.Source: §3.2 · pp. 4–5, Figure 1 · p. 5
  • The two-tier model separates dormant learners from active learners before K-means clustering active learners on standardised longitudinal and participation features.The four-cluster solution was chosen as the smallest solution distinguishing rising from tapering patterns; the resulting archetypes describe observed behaviour, not fixed psychological types.Source: §3.2 · pp. 5–6

3.3 Perception Survey and Linkage

  • The perception survey used ten Likert items and two open-ended items, but optional dashboard access and response created two stages of self-selection.Response rates were much lower in the Fast Starters, Occasional, and Dormant groups; those comparisons were treated as exploratory, while pooling low-engagement groups preserved eight significant items.Source: §3.3 · pp. 6–7

4 Data Collection and Analysis

The analysis uses operational records from a rolling-entry remote internship, with participation opportunities varying over time. It cleans and normalises participation data, then examines active-learner patterns, perceptions, divergence, and later outcomes.

4.1 Study Context and the Points System

  • The remote internship used synchronous online sessions and polls, with participation recorded through a points system and completion assessed from a later export.The programme was ongoing at segmentation, so completion outcomes were unavailable in the snapshot itself.Source: §4.1 · p. 7
  • The points ledger included onboarding, attendance, and poll awards, while learners had varying opportunities to earn points across time.The analysis population came from a late-June operational export and rolling-entry roster; a second export supplied later outcome records, including for re-registered learners.Source: §4.1 · p. 7, §4.2 · p. 8

4.3 Data Cleaning, Participation Variables, and Privacy

  • After duplicate persons were consolidated, participation features captured attendance and poll rates, points per eligible session, weekly point velocity, and acceleration.Raw cumulative points were not the primary indicator because they combine participation with exposure time.Source: §4.3 · p. 9

4.4 Analysis

  • Active learners were clustered on standardised trajectory features, while perception differences, behavioural-perception associations, barriers, and later outcomes were examined with separate analyses.Outcome analyses compared the ordered segment label with cumulative points and points per eligible session on the same snapshot; all indicators were treated as participation, not motivation or learning measures.Source: §4.4 · pp. 9–10

5 Results and Discussion

The results distinguish learners with no recorded participation from active learners’ different trajectories, and show that early segments separate later platform behavior. Survey patterns add context, while the discussion cautions that these behavioral indicators do not directly measure motivation or learning.

Participation funnel and segments (RQ1).

  • 1,871 of 3,607 starters were active; clustering yielded four trajectory segments, with Thriving Core rising then plateauing while the other three patterns fell or stayed low.The segments were Thriving Core, Tapering, Fast Starters, and Occasional Participants.Source: Participation funnel and segments (RQ1) · p. 10, Figure 3 · p. 11

Perceptions, reasons, and barriers by segment (RQ2 and RQ3).

  • Eight of ten perception items differed across segments, but effects were small; pooled perception–behaviour correlations were ρ=.09–.14.Among barrier-reporting responses, technical or infrastructure issues were most common (38%); no coded response explicitly attributed non-participation to disinterest. Strong survey self-selection limited representation of lower-participation segments.Source: Perceptions, reasons, and barriers by… · pp. 11–12, §5.3 · p. 16

Segments and later outcomes (RQ4).

  • 93.9% of Thriving Core learners attended at least ten additional sessions, versus fewer than 12% in each of the other three segments; all six outcomes differed (χ2 p < .001).The outcome window ran from 30 June to 14 September 2026.Source: Table 4 · p. 13, Segments and later outcomes (RQ4) · p. 13

5.3 Limitations

5.2 Discussion

  • Trajectory shape added information mainly among learners with similar cumulative points; totals summarize learners at the extremes better than those in the middle.The discussion frames time as a dimension of learner behaviour rather than reducing participation to cumulative level.Source: §5.2 · p. 14
  • Separating dormant from active learners avoids conflating no observable participation with patterns among participants; low or declining trajectories warrant investigation, not a standalone motivation judgment.The categories describe observable programme behaviour, not psychological types.Source: §5.2 · pp. 14–15

6 Conclusion

Cumulative point totals miss how participation changes over time, while longitudinal, opportunity-aware trajectories distinguish patterns among active learners. Early segments predicted later participation and milestones; learner reports add context to behavioural records.

6 Conclusion

  • Trajectory shape adds information beyond cumulative totals chiefly when totals are ambiguous.The two-tier model first separates learners with no recorded participation, then distinguishes patterns among learners with observable participation.Source: §6 · p. 16
  • Segments assigned from the first four weeks predicted continued participation and later milestones over the following eleven weeks.Source: §6 · p. 16
  • Learner perceptions add context, particularly where low recorded participation coexists with reported motivation and external barriers.Source: §6 · p. 16
  • Future work should test trajectory patterns against off-platform learning outcomes, sample under-represented low-participation learners, and replicate the approach in other incentive-based programmes.Source: §6 · p. 16
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