This Innovative Practice Full Paper presents an approach to improve teaching materials and assessment tools for K-12 programming education together by creating a virtuous cycle. Improving teaching materials for computing education is in line with the conference theme “Creating a convergence in engineering education and workforce development”. In this research, coding sheets are used as a part of the teaching materials, and the assessment tools are used to assess the algorithmic thinking ability of the students. The assumption here is that there is a mutual impact between the improvement of teaching materials and that of assessment tools. This kind of strategy is important, especially in the setting of elementary school education in Japan in which conducting a pilot study is difficult. In Japan, the new curriculum guidelines were fully implemented in April 2020 in elementary schools and in April 2021 in junior high schools respectively. They will be implemented in April 2022 in high schools. Since the announcement of the new curriculum guidelines, the boards of education in the local governments have been preparing for the programming education in the schools. However, there are still remaining problems, including the shortage of trained teachers and the lack of good assessment tools. In order to remedy the shortage of trained teachers, we designed some coding sheets for the teaching materials consisting of block parts, servo motors, DC-motors, LEDs, and sensors. In our assessment tools which are supposed to assess students' Computational Thinking ability, we included only the questions for assessing algorithmic thinking ability, as there is a limited amount of time for the assessment in schools. We have used our teaching materials and our assessment tools in some elementary schools. Based on the results of the teaching activities, we discuss the strategy to improve the teaching materials and the assessment tools together.

This Research to Practice Full Paper presents the development of the algorithmic expressions for the assessment tools for assessing algorithmic thinking ability of elementary school children. In Japan, elementary school children will be required to learn computer programming as an interdisciplinary element appearing throughout the curriculum in 2020. The purpose of this programming education is to nurture Computational Thinking (CT) for elementary school children in Japan. However, almost no discussion has been conducted in Japan on how to measure the level of CT an elementary school child has acquired. Since the definition of CT is not very firm, it is not easy to measure the levels of CT. Therefore, several organizations have issued operational definitions of CT. Among the concepts of CT in those operational definitions, Algorithmic Thinking was chosen as a representative of CT, and the assessment tools for evaluating Algorithmic Thinking ability have been developed in this research. The assessment tool was conducted in the experimental Computer Science Unplugged classes and in the control classes in two elementary schools in Japan. There were in total 152 children in the classes, and all of them were 5th grade children. By answering the questions in the assessment tool, each child got a score between 0 and 15. The scores were statistically analyzed.

In this research, a tool for evaluating algorithmic thinking ability of the primary schoolchildren was developed. This tool is based on the three categories of operations used to construct algorithms, namely, sequential operations, conditional branching operations, and iterative operations. Each question in the tool checks to see if the examinee understands the concept of one of the three categories. The tool was developed to evaluate the educational effect of programming education for middle to upper grade (third to sixth grade) primary schoolchildren. Since both Visual Programming Language (VPL) and Textual Programming Language (TPL) could be used, it was required that the tool could be used by both the group of children who use VPLs and the group of children who use TPLs. To make it possible, no programming language appeared in the questions in the tool. The teaching materials for the programming education were also developed in such a way that the three basic concepts of algorithm, namely, sequential processing, conditional branching, and repetitive processing, were clearly taught. The target VPL in this research was Scratch. The evaluation tool was conducted in a weekend class of programming education for primary schoolchildren, and the algorithmic thinking ability of the schoolchildren was analyzed.

The purpose of this research is to compare textual programming languages and visual programming languages from the aspect of motivation. As a textual programming language, Processing programming language was used, and as visual programming languages, Scratch, a derivation of Scratch, Teaching materials offered by code.org, and LEGO Mindstorms EV3 were used. Teaching materials using the textual programming language, and those using the visual programming languages were developed separately. A trial experiment of programming education with the textual programming language was conducted to a cohort of seven primary schoolchildren. Trial experiments with the visual programming languages were conducted twice. In each of them, a cohort of eight primary schoolchildren participated. The motivation of the children was assessed using the questionnaire based on the ARCS (Attention, Relevance, Confidence, and Satisfaction) motivation model. The results with the visual programming languages suggested that the motivation scores of the children increased as the class progressed when visual programming languages were used. On the other hand, the results with Processing suggested that the variance of Satisfaction factor increased as the class progressed when textual programming languages were used, which further suggested that the Satisfaction scores of the children spread as the class progressed when textual programming languages were used.

In this research, a Textual Programming Language (TPL) is used in programming education for primary schoolchildren because of the following reasons: (1) it is more practical to use the programming languages similar to the ones used for developing real applications, (2) typing statements could be easier for primary schoolchildren than generally thought, (3) there exist programming environments such as Processing that are easy to use and produce very attractive graphical outcomes. Teaching material for programming education with Processing was developed. In this teaching material, cartoons were used to explain difficult concepts. The learners who use this teaching material were supposed to draw some computational figures with chosen colors. Trial experiments of programming education using this teaching material was conducted to a cohort of seven primary schoolchildren (six 4th grade and one 5th grade children) in two consecutive weekend classes (one hour each). Since the authors' aim of this programming education was to create a sense of fun and excitement in the children and inculcate a desire to engage with computing, the motivation of the children was assessed using the questionnaire based on the ARCS (Attention, Relevance, Confidence, and Satisfaction) motivation model. The results were encouraging and suggested that TPLs could be used in programming education for primary schoolchildren.

In the past, the authors had been analyzing motivation of the learners in programming education using the ARCS assessment metric. This metric had been used in the application experiment in 13 programming courses, and about 1,700 sets of data was collected. From these data, the learners' model, characteristics of the change of motivation, and ways of improving teaching materials had been clarified. However, these study results were obtained after the terms, when the programming courses were over, and thus did not contribute much to the ongoing programming education. For this reason, in this research, the methods for predicting the change of learners' motivation were studied so that the learners who may need support could be identified. The idea came from the experiment the authors conducted, in which the motivation of learners was analyzed by plotting the motivation scores of each factor in the ARCS model as a 3D graph. As a result, a decreasing tendency of motivation was observed when the distribution of the plot widened. After studying the tendency in detail, it was thought to be due to the influence of the variance of sub-level category scores. In the proposed method, the motivation of each learner is assessed in each lesson using the ARCS assessment metric. If variance of the motivation scores of a learner in a lesson is above a certain threshold value AND if mean of the scores has not decreased from the previous lesson, then the learner is identified as a candidate of learner who needs support at that lesson. In the application experiment, a programming course with 9 lessons was offered and 9 learners attended all the 9 lessons. In the experiment, 7 cases had been identified as the candidates of learners who need support, and out of those 7 cases, a decrease of motivation to less than average was observed in 5 cases.

In this paper, a method for improving the teaching materials of programming education is introduced, and the evaluation of the effects of using the strategy is presented. By using this method, the teachers of programming education will be able to assess and improve their teaching materials irrespective of their knowledge and experience of their teaching materials already used. In this method, the teaching materials were improved based on the statistical analysis of the motivation of students. Specifically, the motivation of students was measured for each lower category of ARCS motivation model with the authors' original questionnaire. The lower category in a particular lesson that showed a statistically significant decrease from the previous lesson was identified, and the improvement strategies for the lower category were selected from the list of motivation strategies in the ARCS model. The teaching materials of programming education were then improved based on the strategy. In this research, five lower categories of particular lessons in a programming course were identified, and the teaching materials were improved. The improved teaching materials were used in the following programming course, and the effects of the improvements were seen in three lower categories out of the identified five lower categories.

In this research the motivational levels of the students in a social welfare department while learning computer programming were analyzed relative to the contents that were taught in each lesson. The introductory programming course was game-based, and Java language was used with Eclipse as the Integrated Development Environment (IDE). The lessons were designed in such a way that as the students finished more and more assignments they were nearing completion of the Tetris game. The motivation levels were measured using a questionnaire based on the ARCS motivation model, which has four factors: Attention, Relevance, Confidence, and Satisfaction. As a result, it was found that the motivation of the students changes according to the lesson content.

In this research, the motivational aspects of the students while learning introductory programming was monitored and analyzed. The students were in the social welfare department, and the programming language used was Java and Processing. Monitoring students' motivation throughout the course was found to be important because, by doing so, the weak points in the syllabus could be found and modified. It was also found to be important to devise topics relevant to the students, and to adjust the level of difficulty of the assignments so that the students can feel confident studying.

In order to plan efficient and effective in-house training for programming, the characteristics of the motivation of learners in an in-house training of programming course, in an information and communication technology (ICT) industry, have been analyzed. In the in-house training course, 42 participants underwent a 16 day training program of Java language. The analysis of the motivation was carried out using a questionnaire based on the ARCS motivation model. As a result, it has been found that the motivation of those who have a technology background is not necessarily higher than that of those who have a non-technology background.

In March 2008, the Japanese government announced the revision of the teaching guidelines for junior high schools, which are to be fully enacted in April 2012. According to the new guideline for the subject of Technology Education, “measurement and control by programming”, “growth of living things”, and “energy transformation”, which had previously been elective topics, are to become mandatory. Therefore, it will be necessary to develop compound teaching materials that can be used to teach these topics. In this research, we developed practical teaching material synthesizing these topics. The developed teaching material is an automatic watering system in which soil water is measured and used as feedback to control the watering mechanism. As a preliminary test before introducing this teaching material in junior high schools, we administered mock lessons to university students five times. The average scores for all the question items improved after the lesson, and the improvement was particularly remarkable for the questions about acquiring knowledge.

In this research, we have analyzed the transition of the motivation of the students in the art design faculty to learn programming using the questionnaire based on the ARCS motivation model. As a result, we have found that the change of the motivation of the students had been more prevalent in the first half of the course than the second half of the course.

The purpose of this paper is to analyze the relation between the teaching materials and motivation to learn programming when the results of programming are works of art, and the students are from the art and digital design faculty. The programming environment used in this study was Processing which can produce fine artwork with relatively simple codes. The programming courses with Processing were offered at two universities, and the SIEM assessment standard was used to evaluate the students' motivation levels to learn programming. It has been verified from the analysis of the SIEM assessment standard that the desire of the students to create aesthetically satisfying artwork is important for the motivation to learn programming. Also, we have outlined the items that affected the change of the motivation index using the statistical analysis method.

In order to analyze the factors that raise the motivation of students in the art design faculty and digital design faculty to learn programming, a programming course using processing programming environment was offered to the students at two universities. The teaching materials used were designed in accordance with the ARCS motivation model, and the SIEM assessment standard was used to evaluate students' motivation levels. It has been found that in order to maintain or raise students' motivation it is more important to allow students to enjoy seeing the final results (artwork) of the programming than to make them strive to create more beautiful artwork.

The data compression based on dictionary techniques works by replacing phrases in the input string with indexes into some dictionary. The dictionary can be static or dynamic. In static dictionary compression, the dictionary contains a predetermined fixed set of entries. In dynamic dictionary compression, the dictionary changes its entries during compression. We present parallel algorithms for two parsing strategies for static dictionary compression. One is the optimal parsing strategy with dictionaries that have the prefix properly, for which our algorithm requires O(L+log n) time and O(n) processors, where n is the number of symbols in the input string, and L is the maximum length of the dictionary entries, while previous results run in O(L+log n) time using O(n/sup 2/) processors or in O(L+log/sup 2/ n) time using O(n) processors. The other is the longest fragment first (LFF) parsing strategy, for which our algorithm requires O(L+log n,) time and O(n log L) processors, while a previous result obtained an O(L log n) time performance on O(n/log n) processors. For both strategies, we derive our parallel algorithms by modifying the on-line algorithms using a pointer doubling technique.

Studies parallel algorithms for two static dictionary compression strategies. One is the optimal dictionary compression with dictionaries that have the prefix property, for which our algorithm requires O(L+log n) time and O(n) processors, where L is the maximum allowable length of the dictionary entries, while previous results run in O(L+log n) time using O(n/sup 2/) processors, or in O(L+log/sup 2/n) time using O(n) processors. The other algorithm is the longest-fragment-first (LFF) dictionary compression, for which our algorithm requires O(L+log n) time and O(nL) processors, while the previous result has O(L log n) time performance on O(n/log n) processors. We also show that the sequential LFF dictionary compression can be computed online with a lookahead of length O(L/sup 2/).