Predicting Spatial Visualization Problems' Difficulty Level from Eye-Tracking Data.

The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.

Engineering Student's Ethical Awareness and Behavior: A New Motivational Model.

Professional communities are experiencing scandals involving unethical and illegal practices daily. Yet it should not take a national major structure failure to highlight the importance of ethical awareness and behavior, or the need for the development and practice of ethical behavior in engineering students. Development of ethical behavior skills in future engineers is a key competency for engineering schools as ethical behavior is a part of the professional identity and practice of engineers. While engineering educators have somewhat established instructional methods to teach engineering ethics, they still rely heavily on teaching ethical awareness, and pay little attention to how well ethical awareness predicts ethical behavior. However the ability to exercise ethical judgement does not mean that students are ethically educated or likely to behave in an ethical manner. This paper argues measuring ethical judgment is insufficient for evaluating the teaching of engineering ethics, because ethical awareness has not been demonstrated to translate into ethical behavior. The focus of this paper is to propose a model that correlates with both, ethical awareness and ethical behavior. This model integrates the theory of planned behavior, person and thing orientation, and spheres of control. Applying this model will allow educators to build confidence and trust in their students' ability to build a professional identity and be prepared for the engineering profession and practice.