1. Temporal datasets
In this datasets students were able to answer the same question several times.
**Assistments 2009-2010**
**Berkeley** -1730  students tried 234 questions that were from online CS course. 

2. Non-temporal datasets
In this case all students tried all questions.
**Castor** and **Fraction** - middle school students datasets.
**TIMSS** - 757 students and 23 math questions.
**ECPE** -  2922 students and 28 language related tasks.

Datasets (Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing)

Based on student ids items and outcomes the authors generate the number of successful/unsuccessful attempts. They use 5-fold cross validation. and divide data into train and test sets. Non temporal datasets are trained for 1000 epochs , assistments - 500 and Berkeley - 300.

Framework (Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing)

San Diego State University

1. Introduction
2. Related Work
3. Knowledge Tracing Machines
4. Experiments
5. Results and Discussion
6. Future Work

Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing

To improve learning processes on online educational platforms, accurately modeling student knowledge is essential. A significant challenge arises when trying to measure knowledge across students who have attempted different subsets of questions. Traditional student learning models often rely on sequence prediction or factor analysis, typically using unidimensional parameters for students or questions. The Knowledge Tracing Machines (KTM) framework addresses these limitations by generalizing these models to higher dimensions (up to twenty). Additionally, KTM leverages side information by identifying the specific knowledge components involved in each question—often structured as a Q-matrix—to better capture complex learning interactions.

Introduction (Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing)

1. paper: https://arxiv.org/pdf/1811.03388.pdf

2. code: https://github.com/jilljenn/ktm

Reference for Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing

1. Knowledge Tracing
2. Factor Analysis
3. Item Response Theory
4. AFM(additive factory models) and PFA (performance factory analysis)
5. Factorization Machines

Related Work (Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing)

1. General
2. Data and encoding of side information
3. Relation to existing models
4. Training
5. Visualizing Embeddings

Knowledge Tracing Machines (Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing)

Experiments (Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing)

On the assistments dataset KTM  training was much faster than DKT (deep knowledge tracing). It was observed that wins and fails (performance factory analysis) encoding improves performance a lot compared to encoding the number of attempts (additive factor model). When the bias was added for all the items the performance improved in the case of IRT (item response theory) but same can't be said about PFA (performance factory analysis). This is explained by the fact that the amount of items is large and adding difficulty parameter would be helpful in this case. For some of the datasets it was easy to identify skills but for others it was difficult which was caused by the fact that there were too few skills or only one KC was associated with the item. Increasing dimensions only slightly increased the performance when temporal datasets were used.  

Results and Discussion (Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing)

The authors made predictions in test set on totally new students. In this paper only pairwise interactions were provided and for the future research it would be interesting to generalize interactions to k-way terms. Instead of binary outcomes it might be interesting to consider graded outcomes.

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