Abstract: Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and classic Markovian models such as Bayesian Knowledge Tracing (BKT) have been successfully applied for student modeling. However, much of this prior work is designed to handle sequences of events with discrete timesteps, rather than considering the continuous aspect of time. Given that time elapsed between successive elements in a student’s trajectory can vary from seconds to days, we applied a Timeaware LSTM (T-LSTM) to model the dynamics of student knowledge state in continuous time. We investigate the effectiveness of T-LSTM on two domains with very different characteristics. One involves an open-ended programming environment where students can self-pace their progress and T-LSTM is compared against LSTM, Recent Temporal Pattern Mining, and the classic Logistic Regression (LR) on the early prediction of student success; the other involves a classic tutor-driven intelligent tutoring system where the tutor scaffolds the student learning step by step and T-LSTM is compared with LSTM, LR,more »
Deep Learning vs. Bayesian Knowledge Tracing: Student Models for Interventions.
Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling tasks: post-test scores prediction and learning gains prediction. Additionally, while previous work on student learning has often used skill/knowledge components identified by domain experts, we incorporated an automatic skill discovery method (SK), which includes a nonparametric prior over the exercise-skill assignments, to all three models. Thus, we explored a total of six models: BKT, BKT+SK, IBKT, IBKT+SK, LSTM, and LSTM+SK. Two training datasets were employed, one was collected from a natural language physics intelligent tutoring system named Cordillera, and the other was from a standard probability intelligent tutoring system named Pyrenees. Overall, our results showed that BKT and BKT+SK outperformed the others on predicting post-test scores, whereas LSTM and LSTM+SK achieved the highest accuracy, F1-measure, and area under the ROC curve (AUC) on predicting learning gains. Furthermore, we demonstrated that by combining SK with the BKT model, BKT+SK could reliably predict post-test scores more »
- Award ID(s):
- 1651909
- Publication Date:
- NSF-PAR ID:
- 10136454
- Journal Name:
- Journal of educational data mining
- Volume:
- 10
- Issue:
- 2
- Page Range or eLocation-ID:
- 28-54
- ISSN:
- 2157-2100
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs.bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants ofmore »
-
Mobile devices are becoming a more common part of the education experience. Students can access their devices at any time to perform assignments or review material. Mobile apps can have the added advantage of being able to automatically grade student work and provide instantaneous feedback. However, numerous challenges remain in implementing effective mobile educational apps. One challenge is the small screen size of smartphones, which was a concern for a spatial visualization training app where students sketch isometric and orthographic drawings. This app was originally developed for iPads, but the wide prevalence of smartphones led to porting the software to iPhone and Android phones. The sketching assignments on a smartphone screen required more frequent zooming and panning, and one of the hypotheses of this study was that the educational effectiveness on smartphones was the same as on the larger screen sizes using iPad tablets. The spatial visualization mobile sketching app was implemented in a college freshman engineering graphics course to teach students how to sketch orthographic and isometric assignments. The app provides automatic grading and hint feedback to help students when they are stuck. Students in this pilot were assigned sketching problems as homework using their personal devices. Students weremore »
-
Deep Reinforcement Learning (DRL) has been shown to be a very powerful technique in recent years on a wide range of applications. Much of the prior DRL work took the online learning approach. However, given the challenges of building accurate simulations for modeling student learning, we investigated applying DRL to induce a pedagogical policy through an offiine approach. In this work, we explored the effectiveness of offiine DRL for pedagogical policy induction in an Intelligent Tutoring System. Generally speaking, when applying offiine DRL, we face two major challenges: one is limited training data and the other is the credit assignment problem caused by delayed rewards. In this work, we used Gaussian Processes to solve the credit assignment problem by estimating the inferred immediate rewards from the final delayed rewards. We then applied the DQN and Double-DQN algorithms to induce adaptive pedagogical strategies tailored to individual students. Our empirical results show that without solving the credit assignment problem, the DQN policy, although better than Double-DQN, was no better than a random policy. However, when combining DQN with the inferred rewards, our best DQN policy can outperform the random yet reasonable policy, especially for students with high pre-test scores.
-
There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than formore »