Jointly Predicting Courses and Grades Using a Transformer-Based Model

Paul Savala

REVIEW 3 major objections 5 minor 45 references

Jointly Predicting Courses and Grades Using a Transformer-Based Model

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read TRACE, a Transformer that jointly predicts next-semester courses and grades, cuts grade-prediction mean absolute error by 46.4% compared with an identical grade-only architecture.

desk verdict Joint course-grade prediction is a plausible and honest contribution, but the headline 46% gain over the grade-only baseline is likely inflated by an undertrained comparator. read the letter →

arxiv 2608.13409 v1 pith:JPFUAYWB submitted 2026-08-13 cs.AI

Paul Savala This is my paper · ORCID

classification cs.AI

keywords learninganalyticseducationaldatamininggradepredictioncoursetransformermulti-taskset-basedlossconcurrency

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

TRACE is a Transformer model that, given a student's major and past semesters of courses and grades, predicts both the set of courses the student will take next semester and the grade in each course. The paper argues that this joint formulation is the key to accurate grade forecasts: an otherwise identical Transformer that predicts grades alone has a mean absolute error of 0.2496, while TRACE reaches 0.1339, a 46.4% reduction, with mean squared error 3.5 times lower. Predicting courses is treated not as an advising feature but as a training mechanism that forces the model to learn meaningful course representations, in the spirit of auxiliary tasks in pretrained language models. The model also outperforms LSTM-based sequence models, a graph neural network, and XGBoost on ten years of institutional data.

What carries the argument

The load-bearing machinery is a semester-positional encoding combined with a set-based multi-task loss. In TRACE, position is the relative semester number, so all courses in the same semester share the same position vector and the model sees the term as an unordered basket, while temporal order across terms is preserved. Embeddings for courses, grades, and major are learned jointly, and a Transformer encoder-decoder with causal masks over semesters produces, for each upcoming semester, a distribution over all course tokens together with a grade for each. The loss uses KL divergence for the course-set distribution, treating the actual course basket as a normalized probability vector, and MSE for grade regression, with a weighting factor $\omega \approx 1$; this avoids the order artifacts of token-level cross-entropy and avoids the per-step assignment cost of Hungarian matching. One-step-ahead training turns each student's history into many input-output pairs, and padding masks keep the loss focused on real courses.

What would settle it

Run OnlyGradesTransformer and TRACE over multiple random seeds with per-model hyperparameter search, reporting learning rate, layer count, and the weighting omega; if a tuned grade-only model reaches TRACE's mean absolute error of about 0.1339, the claimed benefit of joint course prediction collapses.

Watch

Extended reading notes

Core claim

The central claim is that the structural properties of academic history, particularly courses taken concurrently within a semester, can be modeled directly and that predicting the next semester's course set simultaneously with grades is a powerful regularizer for grade prediction. TRACE encodes every course with a positional vector tied to its semester, making all courses in one term permutation-invariant, and uses a loss $\mathcal{L}_{\mathrm{total}} = \mathcal{L}_{\mathrm{course}} + \omega \mathcal{L}_{\mathrm{grade}}$, where the course term is a KL divergence between the predicted distribution over the course catalog and the normalized one-hot vector of actually taken courses, and the grade term is mean squared error on the grades in those courses. The result on a held-out 10% of students is MAE 0.1339 and MSE 0.0392, versus 0.2496 and 0.1400 for the grade-only Transformer; the same architecture with the course head removed loses most of its accuracy. The grade-only comparison is the paper's central evidence that the auxiliary course-prediction task, not the Transformer architecture alone, drives the improvement.

Load-bearing premise

The grade-only Transformer baseline was trained under the same hyperparameters and stopping conditions as TRACE, so the large error gap is attributed to the course-prediction head rather than to a tuning imbalance.

Editorial extensions

If this is right

If TRACE's result holds, forcing a grade predictor to also forecast the next semester's course basket is a cheap way to cut grade-forecast error roughly in half, with no additional data.
A semester-level concurrency encoding gives institutions a general multi-course model that can be retrained on their own records without hand-built prerequisite rules.
The reported grade MAE of 0.1339 on a [0,1] scale corresponds to about 0.1568 on a 4.0 GPA scale, roughly half of a typical plus/minus grade step, suggesting early-alert systems can use these forecasts for triage.
The KL-based set loss provides a template for any task that predicts a variable-size unordered set alongside regression targets, not only for course baskets.
Because the model learns course embeddings from enrollment data alone, it can be adapted to a new institution by retraining and recalibration, without institution-specific feature engineering.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's ablation shows that removing major from the input slightly improves performance; a natural extension would be to test whether the course-prediction head still helps in settings where course choice is highly constrained, such as single-major programs.
A cheap diagnostic would replace the course-set head with a random auxiliary classification task; if grade MAE still drops sharply, the benefit may be generic regularization rather than course-specific representation learning.
The same semester-positional encoding idea could transfer to other domains where events arrive in temporal bundles, such as healthcare procedures or e-commerce baskets, provided the bundle-composition task is predictive of the regression target.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: TRACE is a reasonable applied contribution. The permutation-invariant semester encoding (same positional vector for all courses in a term) and the KL-based set loss are sensible, and the paper is honestly written with a decent limitations section. But the headline comparison against OnlyGradesTransformer is not trustworthy as reported: that baseline's MAE of 0.2496 is far worse than the LSTM baselines (~0.16), which strongly suggests it was undertrained or mistuned. If a properly tuned grade-only Transformer lands near the LSTM level, the 46.4% advantage shrinks to roughly 15–20%. That still leaves TRACE ahead of the other baselines, but the paper's mechanistic claim—that forcing course prediction is what helps—becomes much weaker.

What is genuinely new: the same-position encoding for all courses within a semester, and the normalized KL divergence over course sets as a training target, with a nice ablation showing BCE gives nearly the same grade accuracy. The authors also include a detailed GNN baseline and are transparent about single-institution data and cold-start issues. Code is linked, though the data is mostly private.

Soft spots, in order of severity. First, the grade-only baseline is the load-bearing comparison and it is underdocumented: no learning rate, no omega tuning, no convergence check, no repeated seeds. The LSTM comparison makes this worse, not better, because it implies an identical architecture with less capacity beats the baseline. Second, a single 90/10 split with no confidence intervals; run-to-run variance could explain part of the gap. Third, Section 4.2 calls the MSE (0.0392) the MAE; it is a typo, but it undercuts confidence in the prose. Fourth, the omega weight is set "near one" with no tuning details.

None of this sinks the paper. The method is sensible, the results are directionally consistent across the LSTM and GNN baselines, and the limitations are handled honestly. But the headline number should be treated as an upper bound until the baseline is fixed. This paper is for learning analytics researchers and institutional research offices; a serious referee should be engaged, but the revision needs re-run baselines with proper tuning and report variance. I would bring it to a reading group as a case study in baseline fairness.

Referee Report

3 major / 5 minor

Summary. The paper introduces TRACE, a Transformer-based encoder-decoder model that jointly predicts the set of courses a student will take in the next semester and the grades they will receive in those courses, given their major and prior course/grade history. Courses taken in the same semester share a positional encoding, making the within-semester representation permutation-invariant, and the training loss combines a KL divergence over the course-set distribution with a grade MSE term. Using ten years of data from a single private university, the authors report that TRACE achieves a grade MAE of 0.1339, a 46.4% reduction relative to a grade-only Transformer baseline, and that it also outperforms LSTM, GNN, and XGBoost baselines. The paper frames the auxiliary course-prediction task as a representation-learning mechanism and releases code and an anonymized data subset.

Significance. If the central comparison is trustworthy, the paper makes a useful contribution to learning analytics: it gives a concrete way to encode concurrent semester enrollments in a Transformer, proposes a set-based KL loss that avoids order artifacts, and provides evidence that an auxiliary course-prediction task can improve grade regression. The evaluation design has a notable strength: grade error is computed only for ground-truth enrolled courses, so the reported grade metrics are not contaminated by course-selection accuracy. The release of training code and anonymized data is also a plus for reproducibility. However, the headline 46.4% improvement rests on a baseline whose training is not documented well enough to be a fair comparator, and the reported differences come from a single 90/10 split without variance or significance testing. These issues must be resolved before the magnitude of the claimed benefit can be accepted.

major comments (3)

[Section 3.2 / Table 1] The central claim of a 46.4% MAE reduction is not supported as reported because the OnlyGradesTransformer baseline is not documented sufficiently to be a fair comparator. The baseline is described in one sentence in Section 3.1, with no learning rate, number of layers or heads, embedding dimension, convergence criterion, or whether the loss weight omega in Eq. (4) was tuned for it; all models are fixed at 20 epochs, and no repeated runs or error bars are given. The baseline's MAE of 0.2496 is roughly 55% worse than the two LSTM baselines (0.1608 and 0.1619) despite using the same input and more capacity, which is consistent with an undertrained or mistuned baseline rather than an architectural limitation. Please provide full baseline details, tune the grade-only Transformer comparably (including omega and learning rate), report results over multiple seeds with mean and standard deviation, and test the MAE/MSE differences for significance; without this, the headline improvement cannot be distinguished from tuning imbalance or run-to-run variance.
[Section 2.3.2] The one-step-ahead training procedure is inconsistent with the next-semester prediction task defined in Section 2.1. Section 2.1 states that the output is the courses and grades of the immediately following semester, but the example in Eq. (6) sets the target for input (<SOS>, c1) to (c2,...,cn,<EOS>), i.e., all remaining courses and grades across all future semesters. Please clarify whether the model is trained to predict only the next semester or the entire remaining suffix, and describe how the Table 1 grade metrics are restricted to the ground-truth next semester. This is essential for interpreting the reported errors and for comparing TRACE with the baselines, which may use different target horizons.
[Section 4.2] The early-alert paragraph misreports the main metric. It states 'Our model achieves a grade prediction MAE of 0.0392 on a [0,1] GPA scale,' but Table 1 lists MSE=0.0392 and MAE=0.1339. The subsequent conversion to a [0,4] scale (0.1568) is therefore also incorrect; the correct conversion of the MAE is 0.1339*4 = 0.5356. Please correct the text and re-assess the claim that predictions are accurate to about half of a +/- grade.

minor comments (5)

[Section 2.2.1] The label encoding of course names is said to be in 'random order'; please specify the random seed or state why the ordering cannot affect learned embeddings.
[Section 2.3.1] Eq. (4) uses omega only on the grade loss; please state whether omega was tuned separately for each model (especially OnlyGradesTransformer) and report the selected values, since this affects the comparison.
[Section 3.2.1] The major-removal ablation reports that removing major improves performance, but without repeated runs it is unclear whether the difference (0.1339 vs 0.1327 MAE) is meaningful; a variance estimate would help.
[Throughout] Please fix typos: 'Kullback-Liebler' should be 'Kullback-Leibler' (Section 2.2.9), 'binary cross entry' should be 'binary cross entropy' (Section 2.3.1), 'and and node embeddings' appears in Section 1.1, and the Table 1 caption has a stray space in 'T able 1'.
[Section 3.2] The random-selection baseline of 5/360 = 0.014 for course precision is helpful; consider also reporting the course-loss values (KL or BCE) for TRACE and OnlyGradesTransformer to quantify the auxiliary task's fit.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the grade metrics are computed only on ground-truth courses and the auxiliary course-prediction head is not used at inference, so the central comparison is not self-referential.

full rationale

The paper's central claim is that jointly predicting courses and grades improves grade prediction compared to an otherwise grade-only Transformer. The comparison is explicitly protected against circularity in Section 3.2: grade metrics are "computed over the student's actual enrolled courses" and the model's predicted grade is extracted "for each course the student actually took, so course prediction accuracy does not influence the reported grade error." The OnlyGradesTransformer baseline still receives course embeddings as input but does not predict courses, so the comparison isolates the auxiliary course-prediction head rather than defining the outcome in terms of it. The loss in Eq. (4) combines course KL divergence and grade MSE with a fixed scalar omega; omega is a hyperparameter chosen by experiments, not a quantity fitted to the test labels and renamed as a prediction. No load-bearing step depends on a self-citation, an imported uniqueness theorem, or an ansatz smuggled in through the author's prior work. Concerns that the OnlyGradesTransformer baseline may be undertuned (its MAE of 0.2496 is much worse than the LSTM baselines) are soundness or reproducibility issues about whether the baseline is a fair comparator, not circularity in the derivation. Because the evaluation protocol is self-contained and the auxiliary task is not used at inference, the paper does not reduce to its own inputs.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim depends on several hand-chosen thresholds and a loss weight omega that is tuned by experiment rather than cross-validation. No new physical or conceptual entities are introduced; the model's 'inventions' are architectural and loss-design choices, not unexplained theoretical constructs.

free parameters (4)

omega (loss weight) = near one
Balances course KL loss and grade MSE loss in Eq. (4); chosen via experiments rather than cross-validation.
course count filter threshold = 10 to 60 courses
Students outside this range excluded from the dataset; hand-chosen thresholds in Section 2.2.1.
rare course threshold = 100 enrollments
Courses with fewer than 100 enrollments mapped to the <OTHER> token; hand-chosen in Section 2.2.2.
rare major threshold = 500 students
Majors with fewer than 500 students mapped to the <OTHER> token; hand-chosen in Section 2.2.2.

assumptions (3)

domain assumption Course enrollment in a semester is accurately represented as a normalized categorical distribution over the course catalog.
This is the basis of the KL loss in Section 2.3.1; if enrollment probabilities depend on student-specific constraints, the distributional target may bias the model.
domain assumption The anonymized institutional dataset is representative and free of systematic data errors.
All results rely on this dataset; no data quality checks are described in Section 2.1.
domain assumption GPA values on the 0-to-1 scale preserve an interval structure suitable for MSE regression.
Used in grade regression loss in Section 2.3.1; grade distributions may be ordinal rather than interval-scaled.

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Cite this review

Pith. "Pith review of Jointly Predicting Courses and Grades Using a Transformer-Based Model." pith.science (2026). https://pith.science/paper/JPFUAYWB

@misc{pith2026260813409,
  author       = {Pith},
  title        = {Pith review of: Jointly Predicting Courses and Grades Using a Transformer-Based Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JPFUAYWB}},
  note         = {Machine review of arXiv:2608.13409}
}

read the original abstract

Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses taken within a semester. This simplification can lead to inaccurate performance predictions, particularly for students with heavy or challenging course loads. This paper introduces a TRansformer for Academic Course-grade Estimation (TRACE) that addresses this limitation by jointly predicting both the set of courses a student will take and their corresponding grades for an upcoming semester. Our approach encodes courses on a per-semester basis to capture the effects of course concurrency and utilizes a novel loss function combining course-set prediction with grade prediction. We demonstrate that predicting courses taken in addition to the grades in those courses leads to significant improvements in prediction quality. Trained on ten years of institutional data, our joint prediction model reduces mean absolute error by nearly 50% compared to an identical architecture that predicts grades alone. The model also outperforms traditional LSTM-based sequential models, as well as graph neural network-based approaches, and offers natural ways to incorporate student attribute data. This work demonstrates the utility of modern neural architectures for creating interpretable models that can be adapted to new institutions via retraining and recalibration, as well as the importance of key techniques, such as predicting courses taken during training. We discuss how this model could be incorporated into early detection systems at institutions of higher education.

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