Engineering Education Letters - Volume 2017, Issue 1

Remote laboratory system has been used in engineering education over a decade. To offer a collaborative learning platform for students’ learning, a Wiki-based remote laboratory platform was developed successfully. The effectiveness of the collaborative learning platform was verified by implementing a new remote PID (proportional–integral–derivative) controller experiment based on the Wiki-based remote laboratory platform. This remote experiment aims to offer students hands-on experience to demonstrate the characteristics of proportional, proportional–integral, proportional–derivative and PID controllers and visualize the process of remote tuning. This paper presents the integration of a new remote experiment into an existing mechanical engineering course at the University of Houston. With the help of this remote experiment, students can study the control knowledge actively instead of passively. Moreover, the systematic integration of the Wiki-based remote laboratory platform into laboratory courses reinforces the delivery of content to students with dissimilar learning styles, thus improving students’ success.

This study addresses the learning objectives and the student outcomes of industrial engineering students by examining them at three different levels: course level, program level, and graduate level. Three learning domains are developed and analyzed for this purpose to assess the performance of students during and after graduation. These domains are labeled as the house of cognitive learning, which shows the level of learning, its outcome elements, and the depth of understanding.

In the higher education system, the correct assessment of student learning is always considered as a challenging task. The aim of this study was to develop an integrated integer-programming algorithm to accurately determine the learning level of students. The method incorporates quality control charts and statistical assessment tools to present the findings. In this study, level of learning is calculated as a learning index that presents the contribution of a course to the respective student outcomes. Moreover, it depicts the overall achievements of students during their learning. Therefore, another aim of this study was to explore how to better utilize the collected data for the assessment of learning level. The outcomes of algorithm and statistical approaches are quite encouraging for the evaluation of students' learning, thus improving the quality of engineering program.

The aim of this study was to strengthen the engineering curriculum by integrating innovative electrical and computer engineering (ECE)-specialized artificial intelligence (AI) methodologies and algorithms into traditional civil engineering (CE) problem-solving methods. An interactive and comprehensive knowledge-based expert system (KBES) was developed to document, compare, and analyze cutting-edge AI applications in the field of CE. With a large amount of successful/unsuccessful AI applications being tried and tested in the CE field, this unique intelligent database can be used as the platform and educational media for the development and implementation of curricula for the problem-based learning approach to bridge the gap in the current curricula between conventional mathematics, physics, engineering methods and state-of-the-art AI techniques. This study is the first of its kind to (1) develop an intelligent KBES platform to increase the intellectual rigor, breadth, and depth of undergraduate engineering study and lay a foundation for students pursuing master's degree or PhD in engineering; (2) establish a new interdisciplinary AI curriculum as a capstone course, enrich existing curricula by integrating case studies of AI applications into different levels of undergraduate CE courses, and include knowledge automation software in an ECE course; (3) foster interdisciplinary academic setting that will introduce latest state-of-the-art AI applications to undergraduate students and facilitate their early involvement in research. A brief description of the comprehensive literature search is presented, followed by the proposal of methodologies for the development of the KBES and curriculum. Furthermore, a case study is described to demonstrate the effectiveness and advantages of introducing AI tools into the syllabus of a sophomore CE core course. Students' experiences were assessed and evaluated. The results of the analysis showed that the interdisciplinary curriculum could significantly increase students' awareness on the need for knowledge acquisition, which, in turn, will enhance the learning outcome. The integration of innovative theories and practical applications also improves the problem-solving and critical thinking skills of engineering students, broadens their horizons to new technology, and fosters interdisciplinary settings to better prepare them for diverse and multidisciplinary workforce requirements.