STEAM (Science, Technology, Engineering, Arts & Mathematics) integrated instructional approach not only increases students' interest in learning hard science but also enhances their understanding in arts. We proposed a five-step STEAM interdisciplinary-integrated instructional model to teach student programming, which are: choosing an art example, inspiring creativity, hands-on design, programming implementation, optimizing and sharing. We used the model to design materials to teach middle school student modular programming with the Mandala Art design. The results showed that the proposed STEAM model promoted students' learning, enhanced their understanding of art concepts, and inspired students' thinking.

In this study, we applied STEM approach to teach high school students Internet of Things (IoT) and Artificial Intelligence (AI) concepts with an air quality example. The learning activities include assembling IoT devices for air quality detection, connecting to the MQTT app for data visualization, and applying raw data to build an AI model using TensorFlow to predict air quality. The results showed that the STEM (Science, Technology, Mathematics, and Technology) approach helped students learn data transmission in IoT architecture, modeling process of AI, and enhanced students' motivation in learning.

In this study, we developed an instructional module consisting of four units to help high school students understand the 4V concepts of big data by exploring air pollution. The instructional module enabled students to explore air quality data on specific websites, and asked students to create a "calendar heat map" using open dataset. We taught the module in a high school class and the results demonstrated that students gained knowledge about big data, and became more aware of the impact of air pollution in their lives.

We have developed a quantitative tool for assessing senior high school students' computational thinking skills and administered it in the Taipei metropolitan area of Taiwan. Our CT test is language/tool/knowledge-independent and focuses on four key CT concepts: decomposition, data representation, algorithms, and pattern generalization. Our paper outlines the development process of the CT test, including its constructs, reliability, and validity, and discusses its educational implications for computing courses. This CT test provides a valuable resource for computing teachers who wish to assess their students' CT abilities and tailor their teaching methods accordingly.

In this study we developed three hands-on activities to teach high school students computational thinking (CT) and, specifically, the decomposition skills. The activities were designed to enable students to solve problems by using application tools. The computer science concepts utilized in the activities included binary search, quick sort and iteration. We evaluated the effect of the activities utilizing a post-activity questionnaire, a post-test, students' worksheets, and semi-structured interviews with the participating students. The results indicated that the hands-on activities developed in this study improved students' CT ability.

his study investigates the cognitive processes involved in program comprehension based on eye gaze tracking. Thirty-eight participants were asked to comprehend the test programs. Eye-movement analysis was employed to track the students' gaze paths while they traced the programs for comprehension. Cognitive processes were then obtained by sequential analysis of the gaze data to investigate the significant sequences of attention areas. The mined cognitive processes were compared between high and low comprehenders. The experiment results show that most subjects focused on major part of the program to comprehend the program. However, high comprehenders traced the program in a more logical manner. In addition, low comprehenders needed more manual calculation and also needed to trace back to prior statements for recalling required information, which might be because they had limited working memory capacities for computing and memorizing. Subjects performed better on cognitive processes involved in the working memory (short-term memory, mental arithmetic, and logical thinking) also performed better on program comprehension. Working memory seems to play an important role in program comprehension. These results can provide suggestions for researchers/instructors to develop adaptive instructional strategies for students of different characteristics.

An experimental research design was implemented in an attempt to understand how different types of conceptual models and cognitive learning styles influence novice programmers when learning recursion. The results indicate that in teaching recursion to novice programmers:• concrete conceptual models are better than abstract conceptual models,• novices with abstract learning styles perform better than those with concrete learning styles,• abstract learners do not necessarily benefit more from abstract conceptual models, and• concrete learners do not necessarily benefit more from concrete conceptual models.

In this paper we present some of the experiences that we have gained from having developed a number of laboratory-based courseware for teaching computing concepts.We point out that lab-based teaching is a special category of computer-based instruction (CBI).The intended lab usage imposes certain constraints on lab-based courseware such that the methodologies for developing traditional CBI software do not seem to fit the development of lab-based software very well.Lab-based software requires special considerations as to the presentation of its course content and the design of its user interface.

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