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arxiv: 2604.25049 · v1 · submitted 2026-04-27 · 💻 cs.CY

Adoption of TikTok as a Learning Tool in Physical Education: Evidence from the Philippines

Vanessa B. Sibug , Jan Henry B. Sunga , Emerson Q. Fernando , Roe Vincent S. Ovejas , Arjan Gil S. Mendoza , Trisha Anne A. Onofre , Agnes R. Regala , John Paul P. Miranda This is my paper

Pith reviewed 2026-05-07 17:47 UTC · model grok-4.3

classification 💻 cs.CY
keywords TikTokphysical educationTechnology Acceptance ModelUses and Gratification TheoryPhilippinesstudent adoptionStructural Equation Modelingonline learning tools
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The pith

Perceived usefulness and ease of use most strongly predict Philippine students' intention to adopt TikTok for physical education content.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines what leads tertiary students in the Philippines to adopt TikTok as a source of physical education material. It draws on the Technology Acceptance Model and Uses and Gratification Theory to evaluate survey responses covering information seeking, entertainment, usefulness, and ease of use. Structural equation modeling applied to data from 1,075 regular users shows that perceived usefulness and perceived ease of use stand out as the strongest drivers of intention to use the platform for PE. This finding indicates that short-form video can function as an accessible way to support active participation in physical education within this setting.

Core claim

The study establishes that perceived usefulness and perceived ease of use are the strongest predictors of intention to use TikTok for PE-related content among Filipino tertiary students. This conclusion rests on structural equation modeling of survey data collected from 1,075 regular TikTok users, drawing constructs from the Technology Acceptance Model and Uses and Gratification Theory. The results indicate that TikTok functions as an engaging and accessible medium supporting active learning and participation in physical education.

What carries the argument

Structural equation modeling of Technology Acceptance Model and Uses and Gratification Theory constructs (information seeking, personal identity, social interaction, entertainment, perceived usefulness, perceived ease of use, and intention to use) applied to self-reported survey data from 1,075 participants.

If this is right

  • TikTok can serve as a supplementary tool that increases student participation in physical education through short, accessible video content.
  • Content aimed at PE should emphasize clear usefulness and simplicity to raise students' intention to engage with it.
  • Educational programs in the Philippines gain empirical support for incorporating short-form video platforms into PE instruction.
  • The same combination of usefulness and ease of use may encourage adoption of similar platforms for other forms of active learning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Educators outside the Philippines could test whether the same two predictors dominate when students encounter TikTok-based PE material in different cultural settings.
  • If high intention leads to sustained use, schools might observe measurable gains in daily physical activity levels among students who engage with the platform.
  • Future work could examine whether knowledge and skills acquired through TikTok PE videos persist longer than those from traditional instruction alone.

Load-bearing premise

Self-reported survey responses from regular TikTok users accurately reflect real-world adoption behavior, and the Technology Acceptance Model and Uses and Gratification Theory constructs apply without modification to this cultural and educational context.

What would settle it

A longitudinal study that tracks actual TikTok usage for PE content over one semester and measures whether students who initially rate high on perceived usefulness and ease of use show correspondingly higher usage rates or improved physical education outcomes.

read the original abstract

This study examines the factors that influence the adoption of TikTok as a learning tool for physical education (PE)-related content among tertiary students in the Philippines. The study applies the Technology Acceptance Model (TAM) and Uses and Gratification Theory (UGT) to assess Information Seeking, Personal Identity, Social Interaction, Entertainment, Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Use (IU). A cross-sectional design and Structural Equation Modeling (SEM) were employed. The sample included 1,075 regular TikTok users with an average age of 19 years, the majority of whom were female. The analysis revealed that PU and PEOU were the strongest predictors of IU TikTok for PE related content. The results indicate that TikTok provides an engaging and accessible medium that supports active learning and participation in PE. The study offers empirical evidence from the Philippines and contributes to the academic discussion on the role of short-form video platforms in PE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper examines factors influencing adoption of TikTok as a learning tool for physical education content among Philippine tertiary students. It integrates the Technology Acceptance Model (TAM) with Uses and Gratification Theory (UGT) constructs (Information Seeking, Personal Identity, Social Interaction, Entertainment) and uses structural equation modeling (SEM) on cross-sectional survey data from 1,075 regular TikTok users (mean age 19, majority female) to conclude that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) are the strongest predictors of Intention to Use (IU) TikTok for PE-related content.

Significance. If the central empirical result holds after methodological strengthening, the study supplies useful context-specific evidence from the Philippines on short-form video platforms supporting active learning in PE. The large sample and explicit TAM+UGT integration are positive features; the work could inform educational technology strategies in similar settings, though its cross-sectional design inherently limits claims about actual behavioral adoption.

major comments (3)
  1. [Methods] Methods section: the SEM analysis reports no tests for common method bias despite relying entirely on single-source self-report measures of PU, PEOU, IU, and UGT constructs; this is a load-bearing omission because response artifacts could systematically inflate the observed paths from PU and PEOU to IU.
  2. [Results] Results section: the claim that PU and PEOU are the strongest predictors of IU is presented without model fit indices (CFI, RMSEA, SRMR), standardized path coefficients, or comparison of effect sizes against the other UGT predictors; without these, the relative strength and overall adequacy of the model cannot be verified.
  3. [Discussion] Discussion/Methods: the outcome variable is purely self-reported Intention to Use with no behavioral anchoring (logged usage, pre/post PE performance measures, or observational data); this directly undermines the leap from measured paths to real-world adoption of TikTok as a PE learning tool.
minor comments (2)
  1. [Abstract] Abstract: the demographic description ('majority female') should include the exact percentage and any other key sample characteristics for transparency.
  2. [Methods] The manuscript should explicitly state the software used for SEM, the estimation method (e.g., ML or WLSMV), and any data exclusion criteria applied to the 1,075 respondents.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which have helped us improve the clarity and rigor of our manuscript. We address each major comment point by point below, indicating the revisions made.

read point-by-point responses

  1. Referee: [Methods] Methods section: the SEM analysis reports no tests for common method bias despite relying entirely on single-source self-report measures of PU, PEOU, IU, and UGT constructs; this is a load-bearing omission because response artifacts could systematically inflate the observed paths from PU and PEOU to IU.

    Authors: We agree that common method bias testing is a necessary step when using single-source self-report data in SEM. In the revised manuscript, we have added Harman's single-factor test to the Methods section and report the results (showing that no single factor explains more than 50% of the variance) in the Results section. We have also noted this in the limitations to address potential concerns about inflated paths. revision: yes

  2. Referee: [Results] Results section: the claim that PU and PEOU are the strongest predictors of IU is presented without model fit indices (CFI, RMSEA, SRMR), standardized path coefficients, or comparison of effect sizes against the other UGT predictors; without these, the relative strength and overall adequacy of the model cannot be verified.

    Authors: We acknowledge the need for greater transparency in model reporting. The revised manuscript now includes the model fit indices (CFI, RMSEA, SRMR), all standardized path coefficients, and explicit comparisons of effect sizes demonstrating that the PU and PEOU paths to IU are the largest. These additions substantiate the claim while confirming acceptable model fit, and the Results section has been expanded accordingly. revision: yes

  3. Referee: [Discussion] Discussion/Methods: the outcome variable is purely self-reported Intention to Use with no behavioral anchoring (logged usage, pre/post PE performance measures, or observational data); this directly undermines the leap from measured paths to real-world adoption of TikTok as a PE learning tool.

    Authors: We recognize that self-reported intention serves as a proxy rather than a direct measure of behavior, which is a standard feature of TAM-based studies but does limit direct inferences about actual adoption. We have revised the Discussion to explicitly acknowledge this limitation and to recommend future research incorporating behavioral indicators such as usage logs or performance metrics. Given the cross-sectional survey design and data collected, we cannot retroactively add behavioral measures to the current study. revision: partial

Circularity Check

0 steps flagged

Empirical SEM on external survey data yields no circular derivation

full rationale

The paper collects primary survey responses from 1,075 students and applies the established TAM+UGT framework via SEM to estimate path coefficients. The headline result (PU and PEOU as strongest predictors of IU) is an output of that data-driven estimation, not a re-expression of the input constructs or a fitted parameter renamed as a prediction. No self-citation chain, uniqueness theorem, or ansatz is invoked to justify the model structure; the derivation chain terminates in observable questionnaire items rather than looping back to its own fitted values. This is a standard empirical application with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the Technology Acceptance Model and Uses and Gratification Theory constructs as applied to this population; no free parameters are introduced beyond standard SEM estimation, and no new entities are postulated.

axioms (1)
  • domain assumption TAM and UGT constructs (PU, PEOU, IU, Information Seeking, Personal Identity, Social Interaction, Entertainment) are valid and measurable via self-report Likert scales in this cultural setting.
    Invoked implicitly by applying the models to predict IU without additional validation steps mentioned in the abstract.

pith-pipeline@v0.9.0 · 5513 in / 1241 out tokens · 43714 ms · 2026-05-07T17:47:52.190243+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

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