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.
适应性学习路径 MOOC Felder-Silverman Learning Styles model (FSLSM) quality e-learning Instructional design student engagement learning path eye-tracking measurement adaptive learning system learner profile parameters education for sustainable development Academic self-efficacy MOOCs motivation multiple-choice items Learning engagement Convolutional Neural Networks (CNN) Gated Recurrent Unit (GRU) learning processe 扎根理论 学习分析 Perceived usefulness learning styles identification 大数据 Definition verbalizer/imager learning style 数据挖掘 felder-silverman Monte Carlo模拟 适应性学习支持系统 学习路径 Recurrent Neural Networks (RNN) eye-tracking technology big data Learning persistence 个性化学习 online education neural networks 课堂教学 慕课 Collaborative learning eye-tracking experiment Teaching presence