{"id":"4dfdb81b-87f0-4a29-9b9a-ce880a41c3c3","arxiv_id":"2412.13765","paper_version":2,"verdict":"REJECT","confidence":"HIGH","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"LLM-SEM scores e-learning engagement by adding normalized views, likes, and an LLM-derived sentiment polarity, but offers no validation that the score measures engagement.","lead":"Researchers propose a metric that combines student comment sentiment from large language models with video views and likes to score engagement on e-learning platforms. The paper argues this gives a scalable alternative to surveys, but it does not validate that the score actually tracks student engagement.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"LLM-SEM's central claim that E_v measures engagement is unsupported: Section 4 validates a sentiment classifier, not the metric itself, and no ground-truth engagement comparison appears anywhere in the paper.","rationale":"The reader's weakest_assumption focuses on the sentiment classifier's transfer to educational comments, which is a real component-level risk. My stress-test identifies a broader and more load-bearing gap: even if the sentiment scores were perfectly accurate, the paper offers no evidence that the composite E_v measures engagement. The only experiment is a sentiment-classification benchmark on a generic Arabic sentiment corpus, not a validation of the engagement metric. This supports the reader's REJECT verdict, though via a different emphasis. Because my concern reinforces rather than redirects the rejection, the verdict should remain unchanged. The concrete test above would settle the matter: without a positive correlation to any independent engagement criterion, the central claim cannot stand; with such a correlation, the metric would gain at least preliminary validity.","tokens_in":6964,"tokens_out":2745,"duration_ms":26919,"concrete_test":"Acquire ground-truth engagement labels for a set of educational YouTube videos (e.g., student self-report surveys, expert-coded engagement, or behavioral outcomes such as completion or retention rates). Compute E_v with the paper's pipeline, then measure Spearman rank correlation and classification agreement between E_v and the ground-truth labels. Also compare LLM-SEM against a metadata-only baseline (E'_v = NV_v + NL_v) and against raw views. If LLM-SEM does not correlate with ground truth or does not significantly outperform the metadata-only baseline, the claim that it is an accurate engagement metric is refuted.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's headline claim is that LLM-SEM provides 'a scalable and accurate measure of student engagement.' For that to hold, E_v = NV_v + NL_v + P_v (Section 3.6) would need to track actual learner engagement. Nothing in the paper establishes this. Section 4 is introduced as validating 'the effectiveness of our proposed LLM-SEM framework,' yet the only experiment reported is sentiment classification (Table 1: accuracy, recall, F1 for LLama, Gemma, and fine-tuned RoBERTa). There is no dataset of courses or videos with engagement labels, no comparison against student self-reports, expert ratings, completion rates, retention, or any other engagement criterion. The metric's components are min-max normalized against unspecified dataset-specific extrema, and the thresholds (Ev > 1.5 good; 0.5–1.5 moderate; < 0.5 poor) are asserted without calibration. Additionally, the sentiment component is produced by a model fine-tuned on an Arabic Sentiment Corpora repository, not on educational comment text, so even the qualitative input lacks demonstrated domain validity. The central claim therefore rests on an unvalidated construct: the paper provides no test that distinguishes LLM-SEM from an arbitrary weighted sum of views, likes, and comment polarity.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes LLM-SEM, a metric for measuring student engagement in e-learning videos by combining normalized metadata (views and likes) with sentiment polarity scores derived from LLM-based sentiment analysis of user comments. The methodology covers data collection from YouTube playlists, sentiment classification with fine-tuned RoBERTa and two other LLMs, min-max normalization of metadata, and aggregation into a per-video score E_v. The only empirical evaluation reported is sentiment classification accuracy (Table 1), with no validation of the engagement metric itself against any ground-truth engagement criterion.","tokens_in":7248,"tokens_out":3128,"duration_ms":26382,"significance":"If the proposed metric were validated, it would offer a scalable complement to surveys for monitoring engagement on educational video platforms. The paper's sentiment classification comparison is a useful component-level contribution, and the fine-tuning setup is reproducible from the public Arabic Sentiment Corpora. However, the central claim that LLM-SEM provides an accurate measure of engagement is entirely unsupported by the reported experiments, and the metric is constructed as an equal-weight sum of its inputs with no external validation, which limits the significance of the contribution in its current form.","major_comments":[{"comment":"The central claim that E_v = NV_v + NL_v + P_v is an \"accurate measure of student engagement\" is not tested anywhere in the paper. Section 4 reports only sentiment classification results (Table 1) on the Arabic Sentiment Corpora; there is no dataset with engagement labels, no comparison against student self-reports, expert ratings, completion rates, retention, or any other external engagement criterion, and no baseline engagement metric for comparison. Consequently, the paper provides no evidence that E_v tracks engagement rather than an arbitrary weighted sum of views, likes, and comment polarity.","section":"Section 3.6, Eq. (1)"},{"comment":"The thresholds for engagement quality (Ev > 1.5 good, 0.5–1.5 moderate, <0.5 poor) are asserted without calibration or any principled derivation, and the min-max normalization depends on dataset-specific extrema (Vmin, Vmax, Lmin, Lmax) that are not reported. The equal weighting of the three components (NV_v, NL_v, P_v) is a free parameter whose influence on the resulting engagement classification is not analyzed, so the metric's behavior under different weightings or normalization choices is unknown.","section":"Section 3.5 and Section 3.6"},{"comment":"The sentiment model used for the polarity component is fine-tuned on the Arabic Sentiment Corpora, which is not a collection of educational comments, so the model's transfer to the target domain (YouTube lesson comments) is unvalidated. Additionally, Section 3.3 states that the confidence score \"ranges from -1 to 1\", which conflates confidence (normally in [0,1]) with polarity; the weighting w_c = ±score_c (with neutral mapped to 0) is a signed combination of polarity and confidence whose interpretation is not justified.","section":"Section 3.2 and Section 3.4"},{"comment":"The paper claims that \"extensive experiments\" were conducted to evaluate the LLM-SEM framework, but the only experiment reported is sentiment classification on the fine-tuning/benchmark dataset. There is no evaluation of the full pipeline on actual course or lesson data, so the contribution of each component to E_v and the metric's practical utility remain undemonstrated. The abstract and conclusion overstate the empirical support for the proposed metric.","section":"Section 4"}],"minor_comments":[{"comment":"The model names are inconsistent: the abstract says \"TXLM-RoBERTa\", Section 3.2 says \"Twitter-XLM-RoBERTa-Base for Sentiment Analysis\", and Table 1 says \"RoBERTa (fine-tuned)\"; please use consistent nomenclature.","section":"Abstract and Section 3.2"},{"comment":"The description of the confidence score \"ranges from -1 to 1\" is ambiguous; model confidence is typically in [0,1], and the signed weighting should be described as a separate polarity-weighted confidence score rather than as a raw confidence range.","section":"Section 3.3"},{"comment":"The data collection description lacks essential reproducibility details: the number of channels, the number of playlists/videos/comments, the time period of collection, and the language distribution of the comments are not reported.","section":"Section 3.1"},{"comment":"The notation is confusing because N_v is used both for the number of comments in the polarity calculation and as part of the symbol NV_v for normalized views; consider using distinct symbols (e.g., |C_v| for comment count).","section":"Section 3.4"},{"comment":"The manuscript contains typographical errors and inconsistent numbering (e.g., \"T able 1\" in the results section, and Section 4 lists \"LLama 3.2B\" while Section 3.2 says \"LLama 3B\"); a thorough proofread is needed.","section":"Throughout"}],"recommendation":"reject","confidential_remarks":"The manuscript's central contribution is an unvalidated engagement metric; the only experimental evidence concerns a component (sentiment classification), and the metric itself is an ad-hoc sum of normalized features with asserted thresholds. This is a load-bearing gap that cannot be fixed by local revisions; a substantial validation study would be needed. The paper may be more suitable as a short/position paper after reframing, or after adding a real engagement validation experiment."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The useful part of this paper is narrow but real: the authors compare three LLMs for Arabic sentiment classification on a human-annotated dataset, and the fine-tuned RoBERTa result (0.86 accuracy, 0.84 F1) is a legitimate mini-result. The paper is also clearly written and the pipeline is easy to follow. But the headline claim—that LLM-SEM gives a 'scalable and accurate measure of student engagement'—is unsupported. The metric is simply E_v = normalized views + normalized likes + sentiment polarity, with equal weights and hand-picked thresholds. No ground-truth comparison appears: no student self-reports, no completion rates, no expert ratings, no retention data. The only experiment reported in Section 4 is sentiment classification, which is a component test, not a validation of the engagement metric. The stress-test note is correct: nothing distinguishes LLM-SEM from an arbitrary weighted sum of the same inputs. The sentiment model was trained on a generic Arabic corpus, not on educational comments, so even the qualitative component lacks domain evidence. The confidence-weighted polarity formula and the min-max normalization against unspecified dataset extrema add more free parameters without calibration. I don't think this is incoherent—it's a reasonable first sketch of an engagement heuristic—but it is sold as a validated measure, and that claim does not survive reading. The novelty is modest: combining views, likes, and sentiment is standard in social media analytics, and the paper itself cites prior work doing this outside education. The contribution here is the e-learning application and the Arabic sentiment benchmark. That benchmark is worth something, but it does not carry the engagement claim. My recommendation: desk reject or major revision. If the authors re-frame the paper as 'a proposal with a sentiment component evaluation' and add any real validation against an external engagement criterion, it could be worth another look. As is, I would not send it to review.","headline":"The paper defines an engagement score by summing normalized views, likes, and comment sentiment, but never tests whether that score actually measures engagement—the only experiment validates the sentiment classifier, not the metric.","tokens_in":7738,"tokens_out":1818,"would_cite":false,"duration_ms":18319,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A single engagement scale for e-learning videos is built from views, likes, and comment sentiment.","keywords":["student engagement","e-learning","sentiment analysis","large language models","RoBERTa fine-tuning","YouTube metadata","polarity scoring","LLM-SEM"],"falsifier":"Run LLM-SEM on a set of courses and compare its rankings with an independent engagement signal such as quiz completion rates, video retention, or student survey responses; if the metric's scores do not track that signal, the claim that it measures engagement is refuted.","tokens_in":6779,"feed_emoji":"📊","tokens_out":4917,"duration_ms":44105,"temperature":0.7,"pith_summary":"The paper proposes a student engagement metric, LLM-SEM, that scores e-learning videos by adding normalized view counts, normalized like counts, and the average sentiment polarity of user comments. The aim is to replace surveys and small-scale feedback with a scalable, data-driven measure that captures both how many people interact with a lesson and how they feel about it. To get sentiment right, the authors compare several large language models and fine-tune RoBERTa on a human-annotated Arabic sentiment dataset, reporting that fine-tuned RoBERTa clearly outperforms Llama and Gemma. If the metric works, educators and content creators can compare courses and lessons on a single scale without running questionnaires.","feed_headline":"New metric sums views, likes, and comment sentiment to gauge engagement","feed_subtitle":"Averaging normalized views and likes with sentiment polarity gives course- and lesson-level scores from -1 to 3.","key_machinery":"The load-bearing object is the LLM-SEM formula $E_v = NV_v + NL_v + P_v$, where $NV_v$ and $NL_v$ are min-max normalized views and likes and $P_v$ is the mean sentiment polarity of a video's comments. The polarity stage is the distinctive part: comments are classified by a large language model with a confidence score, positive comments contribute $+score$, negative comments contribute $-score$, neutral comments contribute $0$, and the results are averaged so each video's polarity stays in $[-1,1]$. Playlist polarity is then the mean of the video polarities in a playlist, so courses can be scored by averaging their lessons. The membership function that maps these scores to engagement quality is what translates raw platform data into an assessment.","core_discovery":"The central claim is that a simple additive formula, normalized views plus normalized likes plus a sentiment polarity score, measures student engagement at both lesson and course level. For each video, views and likes are min-max normalized to 0-1, each comment is classified by a large language model as positive, negative, or neutral with a confidence weight, and the polarity $P_v$ is the mean of those weighted scores over the video's comments. The final metric is $E_v = NV_v + NL_v + P_v$, which ranges from $-1$ to $3$, with thresholds at $1.5$ and $0.5$ separating good, moderate, and poor engagement. The paper reports that fine-tuned RoBERTa achieves the best sentiment classification of the models tested (accuracy $0.86$, F1 $0.84$) and builds LLM-SEM on that model.","pith_inferences":["A crucial next step, not taken in the paper, is validating the metric against independent engagement signals such as quiz performance, video retention, or self-reported engagement; the metric's claim to measure engagement rests on that correlation.","The sentiment stage is fine-tuned on general Arabic sentiment data, not on educational comments, so the polarity scores may shift when applied to instructional YouTube comments; a small labeled set of educational comments would test this directly.","The equal weighting of views, likes, and sentiment is an arbitrary design choice that the authors do not justify; different weights could reorder courses, so a sensitivity analysis would show how much rankings depend on that choice.","The metric could be extended, along lines the paper leaves implicit, by adding watch-time, comment volume, or reply counts as additional normalized components."],"forward_implications":["Every lesson can be compared with every other lesson on a single $-1$ to $3$ scale, and courses can be ranked by averaging the engagement scores of their lessons.","The metric runs entirely on public platform data, so it scales to channels and platforms with far more content than any survey-based method could cover.","Fine-tuning a sentiment model on labeled data matters: the fine-tuned RoBERTa model (accuracy $0.86$, F1 $0.84$) clearly outperforms the out-of-the-box Llama and Gemma models.","The stated thresholds give educators a simple triage rule: scores above $1.5$ indicate strong engagement, below $0.5$ poor engagement, and in between moderate engagement."],"supporting_citations":[{"why":"Supplies the Twitter-XLM-RoBERTa sentiment model that is evaluated as one of the candidate LLMs for polarity scoring.","marker":"[4]"},{"why":"Provides the BERT transformer backbone family from which the sentiment models, including RoBERTa, are derived.","marker":"[16]"},{"why":"Supplies RoBERTa, the model architecture that is fine-tuned and reported as the best sentiment classifier in the experiments.","marker":"[17]"},{"why":"Supplies Gemma 9B, one of the large language models compared for sentiment classification accuracy.","marker":"[24]"},{"why":"Supplies Llama, the other large language model compared for sentiment classification accuracy.","marker":"[25]"}],"fun_headline_variants":["LLM-SEM: engagement equals views plus likes plus comment sentiment","Add normalized views, likes, and sentiment to get engagement","Sentiment-powered formula rates e-learning engagement","Course and lesson engagement from views, likes, and LLM sentiment","Engagement score from -1 to 3 using views, likes, and sentiment"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the sentiment scores produced by the fine-tuned model accurately reflect how students feel about a lesson, even though the model was trained on general Arabic sentiment data rather than educational comments.","fun_headline_variants_meta":{"raw":{"variants":["LLM-SEM: engagement equals views plus likes plus comment sentiment","Add normalized views, likes, and sentiment to get engagement","Sentiment-powered formula rates e-learning engagement","Course and lesson engagement from views, likes, and LLM sentiment","Engagement score from -1 to 3 using views, likes, and sentiment"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000806,"raw_usage":{"total_tokens":3524,"prompt_tokens":912,"completion_tokens":2612,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":528,"completion_tokens_details":{"reasoning_tokens":2525}},"tokens_in":528,"tokens_out":2612,"duration_ms":15764,"temperature":1.0,"reasoning_tokens":2525,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T12:48:24.835821+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run LLM-SEM on a set of courses and compare its rankings with an independent engagement signal such as quiz completion rates, video retention, or student survey responses; if the metric's scores do not track that signal, the claim that it measures engagement is refuted.","supporting_citations":[{"cited_title":"In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp","cited_arxiv_id":null,"evidence_quote":"Supplies the Twitter-XLM-RoBERTa sentiment model that is evaluated as one of the candidate LLMs for polarity scoring."},{"cited_title":"In: Proceedings of naacL-HLT, vol","cited_arxiv_id":null,"evidence_quote":"Provides the BERT transformer backbone family from which the sentiment models, including RoBERTa, are derived."}],"review_version":1}