Collaboration is identified as a required and necessary skill for students to be successful in the fields of Science, Technology, Engineering and Mathematics (STEM). However, due to growing student population and limited teaching staff it is difficult for teachers to provide constructive feedback and instill collaborative skills using instructional methods. Development of simple and easily explainable machine-learning-based automated systems can help address this problem. Improving upon our previous work, in this paper we propose using simple temporal-CNN deep-learning models to assess student group collaboration that take in temporal representations of individual student roles as input. We check the applicability of dynamically changing feature representations for student group collaboration assessment and how they impact the overall performance. We also use Grad-CAM visualizations to better understand and interpret the important temporal indices that led to the deep-learning model's decision.
Detection of Disengagement from Voluntary Quizzes: An Explainable Machine Learning Approach in Higher Distance Education
Institute for Research in Open-, Distance- and eLearning, Swiss Distance University of Applied Sciences（开放、远程和e学习研究所，瑞士应用远程大学）；Department of Informatics, Swiss Distance University of Applied Sciences（信息学院，瑞士应用远程大学）；North-West University（北开普省大学）
EPFL（洛桑联邦理工学院）；Sharif University（谢里夫大学）；University of Cagliari（卡利亚里大学）
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We implement five state-of-the-art methodologies for explaining black-box machine learning models (LIME, PermutationSHAP, KernelSHAP, DiCE, CEM) and examine the strengths of each approach on the downstream task of student performance prediction for five massive open online courses. Our experiments demonstrate that the families of explainers do not…
School of Computer Science (National Pilot Software Engineering School)（计算机科学学院（国家示范性软件工程学院））；Beijing University of Posts and Telecommunications（北京邮电大学）
As student failure rates continue to increase in higher education, predicting student performance in the following semester has become a significant demand. Personalized student performance prediction helps educators gain a comprehensive view of student status and effectively intervene in advance. However, existing works scarcely consider the explainability of student performance prediction, which educators are most concerned about. In this paper, we propose a novel Explainable Student performance prediction method with Personalized Attention (ESPA) by utilizing relationships in student profil…
Evaluation of Machine Learning Models in Student Academic Performance Prediction
Dept. of Computer Engineering General Sir John Kotelawala Defence University（计算机工程系 加普尔·约翰·科特拉瓦拉国防大学）；Dept. of Computer Science Sri Lanka Institute of Information Technology（计算机科学系 斯里兰卡信息技术研究所）
Explainable AI and Machine Learning for Exam-based Student Evaluation: Causal and Predictive Analysis of Socio-academic and Economic Factors
Institute of Information Technology Jahangirnagar University（贾汗吉尔纳加尔大学信息科技学院）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Cefalo Dhaka Bangladesh（达卡 Bangladesh 的 Cefalo）
University of Tsukuba（筑波大学）；NTT DATA Corporation（NTT数据公司）
Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use. To that end, feature attribution methods such as SHAP (SHapley Additive exPlanations) are widely used to explain the predictions of black-box machine learning models to customers and developers. However, a parallel trend has been to train machine learning models in collaboration with other data holders without accessing their data. Such models, trained over horizontally or vertically partitioned data, present a challenge for explainable AI because the explai…
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item response theory (IRT) model and a knowledge tracing model that is based on the deep neural network architecture called dynamic key-value memory network (DKVMN) to make deep learning based knowledge tracing explainable. Specifically, we use the DKVMN model to process the student's le…
Knowledge Distillation (KD) has been considered as a key solution in model compression and acceleration in recent years. In KD, a small student model is generally trained from a large teacher model by minimizing the divergence between the probabilistic outputs of the two. However, as demonstrated in our experiments, existing KD methods might not transfer critical explainable knowledge of the teacher to the student, i.e. the explanations of predictions made by the two models are not consistent. In this paper, we propose a novel explainable knowledge distillation model, called XDistillation, thr…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of Iowa（爱荷华大学）；University of Texas（得克萨斯大学）
Advances in data collection in radiation therapy have led to an abundance of opportunities for applying data mining and machine learning techniques to promote new data-driven insights. In light of these advances, supporting collaboration between machine learning experts and clinicians is important for facilitating better development and adoption of these models. Although many medical use-cases rely on spatial data, where understanding and visualizing the underlying structure of the data is important, little is known about the interpretability of spatial clustering results by clinical audiences…
Center for Vision Technologies, SRI International（SRI国际视觉技术中心）；Center for Education Research and Innovation, SRI International（SRI国际教育研究与创新中心）
K-12 classrooms consistently integrate collaboration as part of their learning experiences. However, owing to large classroom sizes, teachers do not have the time to properly assess each student and give them feedback. In this paper we propose using simple deep-learning-based machine learning models to automatically determine the overall collaboration quality of a group based on annotations of individual roles and individual level behavior of all the students in the group. We come across the following challenges when building these models: 1) Limited training data, 2) Severe class label imbala…
Cross-modal Affinity-aligned Multimodal Learning Analytics for Predicting Student Collaboration Satisfaction in Game-Based Learning
Institute of Education, National Cheng Kung University（国立成功大学教育研究所）；Institute of Intelligent System, National Yang Ming Chiao Tung University（阳明交通大学智能系统研究所）；Department of Computer Science, University at Albany, State University of New York（纽约州立大学水牛城分校计算机科学系）
University of Delaware（特拉华大学）；Computer and Information Sciences（计算机与信息科学学院）
Automatic assessment and evaluation of team performance during collaborative tasks is key to the learning analytics and computer-supported cooperative work research. There is a growing interest in the use of gaze-oriented cues for evaluating the collaboration and cooperativeness of teams. However, collecting gaze data using eye-trackers is not always feasible due to time and cost constraints. In this paper, we introduce an automated team assessment tool based on gaze points and joint visual attention (JVA) information extracted by computer vision solutions. We then evaluate team collaborations…
Imperial College London（帝国理工学院）；University of Montenegro（黑山大学）
Forming the right combination of students in a group promises to enable a powerful and effective environment for learning and collaboration. However, defining a group of students is a complex task which has to satisfy multiple constraints. This work introduces an unsupervised algorithm for fair and skill-diverse student group formation. This is achieved by taking account of student course marks and sensitive attributes provided by the education office. The skill sets of students are determined using unsupervised dimensionality reduction of course mark data via the Laplacian eigenmap. The probl…
