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SAINT+: Integrating Temporal Features for EdNet Correctness Prediction
Dongmin Shin, Yugeun Shim, Hangyeol Yu, Seewoo Lee, Byungsoo Kim, Youngduck Choi
TL;DR
Knowledge tracing requires predicting exercise correctness from students’ prior learning interactions. SAINT+ extends SAINT by adding elapsed-time and lag-time embeddings to response representations, and on EdNet it improves SAINT by 1.25% in AUC.
Problem
Knowledge tracing predicts a student’s probability of answering an exercise correctly from previous interaction histories for computer-aided educational applications.
Method
SAINT+ uses a Transformer encoder-decoder that separately processes exercise and response information while adding elapsed-time and lag-time embeddings to response embeddings.
Results
1.25% AUC improvement over SAINT is reported on the EdNet dataset.
Takeaways & Limitations
The EdNet experiments support integrating temporal features into separated exercise and response processing for knowledge tracing.
Takeaways & Limitations
During training, SAINT prevents each position from attending to subsequent positions, so it uses no future sequence information.
Abstract
from arXiv · showhide
We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information. Following the architecture of SAINT, SAINT+ has an encoder-decoder structure where the encoder applies self-attention layers to a stream of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to streams of response embeddings and encoder output. Moreover, SAINT+ incorporates two temporal feature embeddings into the response embeddings: elapsed time, the time taken for a student to answer, and lag time, the time interval between adjacent learning activities. We empirically evaluate the effectiveness of SAINT+ on EdNet, the largest publicly available benchmark dataset in the education domain. Experimental results show that SAINT+ achieves state-of-the-art performance in knowledge tracing with an improvement of 1.25% in area under receiver operating characteristic curve compared to SAINT, the current state-of-the-art model in EdNet dataset.
1 INTRODUCTION
SAINT+ extends the separated Transformer architecture of SAINT with temporal response features and improves knowledge-tracing performance on EdNet.
- SAINT+ extends SAINT by adding elapsed-time and lag-time embeddings to student response representations.Elapsed time is the time taken to answer, while lag time is the interval between adjacent learning activities.
- SAINT separately processes exercise information and student response information using an encoder-decoder Transformer architecture.The encoder processes exercises, while the decoder uses responses and encoder outputs.
- 1.25% AUC gain over SAINT demonstrates improved EdNet knowledge-tracing performance.The comparison is reported against SAINT, described as the current state-of-the-art model on EdNet.
2 RELATED WORKS
Related knowledge-tracing work spans probabilistic, collaborative-filtering, recurrent, memory-based, convolutional, attention-based, and graph-based models.
- Bayesian Knowledge Tracing models evolving latent knowledge with binary mastery states and binary correctness observations.Collaborative Filtering instead represents students and exercises with low-rank latent-factor matrices.
- Deep Knowledge Tracing introduced neural knowledge tracing by using an RNN hidden layer to represent students’ evolving knowledge states.Subsequent models include attention-equipped Bi-LSTMs, text-enhanced exercise representations, and memory-augmented networks.
- Later approaches include hierarchical convolutional models, exponentially decayed attention, Rasch-based embeddings, and graph-based knowledge tracing.These methods model interaction histories, exercise structure, or relationships among knowledge concepts in different ways.
3 KNOWLEDGE TRACING
Knowledge tracing predicts whether a student will answer an exercise correctly from previous interaction histories and associated exercise and response information.
- Knowledge tracing estimates the probability of a correct answer to an exercise from a student’s previous interaction history.
- Each interaction pairs exercise information with response information and related metadata.Response metadata includes correctness, response duration, and the interval since the previous interaction.
- Correctness is represented as c_t ∈ {0, 1}, with 1 indicating a correct answer and 0 an incorrect answer.
4 SAINT+
SAINT+ extends SAINT’s separated Transformer encoder-decoder design by adding elapsed-time and lag-time embeddings to response representations. The model uses masked attention to predict correctness from exercise information and prior responses.
- SAINT architecture: SAINT separates exercise and response sequences, sending exercise embeddings through an encoder and shifted response embeddings through a decoder.The decoder produces estimated correctness probabilities for each exercise from current exercise information and past interactions.
- SAINT architecture: The encoder applies masked multi-head self-attention and feed-forward layers, while the decoder combines masked self-attention with encoder-decoder attention before its final prediction layer.Upper-triangular masking prevents positions from attending to subsequent sequence elements.
- Temporal feature embeddings: SAINT+ augments response embeddings with elapsed time and lag time, preserving the model’s separation of exercise information in the encoder from response information in the decoder.Elapsed time measures response duration, whereas lag time measures the interval between adjacent interactions.
- Temporal feature embeddings: Elapsed-time embeddings use either a continuous learnable scaling vector or categorical vectors assigned to integer seconds, with durations above 300 seconds capped.The continuous representation computes v_et = et·w_elapsed_time; categorical embedding uses one vector per integer second up to the cap.
- Temporal feature embeddings: Lag-time embeddings likewise support continuous or categorical representations, with categorical lag values discretized into 150 trainable vectors covering selected integer-minute intervals.The categorical intervals range from 0 through 1,440 minutes using nonuniform minute values.
5 EXPERIMENTS
Experiments evaluate SAINT+ on updated EdNet-KT1 against benchmark knowledge tracing models and test alternative temporal embeddings, feature combinations, and integration locations. SAINT+ improves over SAINT and performs best when both temporal features are supplied to the decoder.
- 5.1 Dataset: Experiments use the updated EdNet-KT1 dataset, with the most recent 100K students held out for testing and the remaining data split into training and validation sets.The dataset contains problem-solving logs from January 1st, 2019 to June 1st, 2020.
- 5.3 Experimental Setup: SAINT+ is compared with DKT, DKVMN, SAKT, and SAINT using accuracy (ACC) and area under the receiver operating characteristic curve (AUC).The best validation-AUC weight is evaluated on the test set.
- 5.4 Main Results: 2.72% and 3.61% are the maximum ACC and AUC increases achieved by SAINT+ over the benchmark models, respectively.Table 2 reports the performance comparison between SAINT+ and the benchmark knowledge tracing models.
- 5.5.1 Temporal Feature Embedding: Continuous vs. Categorical: The best temporal-embedding result uses continuous elapsed-time embedding and categorical lag-time embedding.The alternatives model each temporal feature either continuously or categorically.
- 5.5.2 Elapsed Time vs. Lag Time: Lag time improves performance more than elapsed time when used alone, but using both features together outperforms either single-feature variant.Both single-feature variants still improve over SAINT.
- 5.5.3 Integrating Temporal Features: Encoder vs. Decoder: Decoder-only integration gives the best performance among decoder-only, encoder-only, and encoder-plus-decoder variants.All temporal-feature variants perform better than SAINT, indicating that the temporal features provide useful information for estimating knowledge status.
6 CONCLUSION
The paper concludes that SAINT+ separately processes exercise and response information while adding elapsed-time and lag-time embeddings to response embeddings. On EdNet, it improves SAINT in both ACC and AUC, with the best results from decoder-input integration.
- 6 CONCLUSION: SAINT+ processes exercise and student response information separately and adds elapsed-time and lag-time embeddings to response embeddings.It retains the Transformer-based knowledge tracing structure of SAINT.
- 6 CONCLUSION: Experiments on EdNet show that SAINT+ improves SAINT in both ACC and AUC.The conclusion reports improvements without specifying the metric values in this passage.
- 6 CONCLUSION: Incorporating temporal features into the decoder input produces the best result and supports separately processing exercise and response information.Future work includes modeling additional learning activities and exploring alternative knowledge tracing architectures.