Adaptive human–computer interfaces (HCIs) are fundamental to designing adaptive websites and adaptive decision support systems. Integrating these intelligent systems with modern eye trackers provides more effective ways to exploit eye fixation data and offers improved services to users. We develop an exemplar-based classifier using the tabu search algorithm to predict which decision strategy may underlie an empirical search behavior. Our algorithm reduces the size of decision concept representations to find the best exemplars for each concept. Experimental results show that our classifier is highly accurate in classifying the sequence of empirical eye fixations, demonstrating the promise of integrating adaptive HCIs with modern eye trackers.
student engagement verbalizer/imager 慕课 eye-tracking technology eye-tracking experiment quality learning style learning styles identification Collaborative learning Learning engagement Monte Carlo模拟 learning processe Perceived usefulness 课堂教学 数据挖掘 MOOCs 适应性学习支持系统 Recurrent Neural Networks (RNN) Teaching presence Convolutional Neural Networks (CNN) 扎根理论 learner profile parameters Instructional design Gated Recurrent Unit (GRU) adaptive learning system motivation 个性化学习 big data neural networks learning path education for sustainable development Learning persistence Felder-Silverman Learning Styles model (FSLSM) 学习分析 Academic self-efficacy eye-tracking measurement felder-silverman e-learning online education 大数据 学习路径 适应性学习路径 multiple-choice items MOOC Definition