Incorporating visual and animation teaching tools in computer programming classes for effective teaching and learning

The problems in teaching and learning programming techniques prevail all over the world. Learning programming logic without utilizing any visualization material is difficult. One possible solution to overcome this problem is by using program animation. The proposed program animation software used in teaching novices learning introductory programming is JELIOT 3. The aim of this project is to investigate the effectiveness of teaching programming classes for beginners using programming tool JELIOT 3 that incorporate visualization and animation, specifically the outcome of students’ performances before and after using JELIOT 3. The study population is novice students with no significant programming experience. They were drawn from an on campus section of second semester Foundation in Business Information Technology introductory programming course at INTI International College, Subang Jaya. The class size when this study was conducted was only 9 students. The 9 students were divided into two groups at random. The first group (POST-JELIOT) received standard classroom lectures then followed by JELIOT 2. The second group (PRE-JELIOT) received the JELIOT 3 lesson and then the traditional classroom lecture. From the findings, there was a significant difference in performance when comparing between students who are first taught JELIOT 3 in the overall assessments on the two programming tests and on the two array model assessments. The implications then are that a visual teaching and animated illustration to programming is an effective method for teaching programming. Instruction in computer programming must make use of such visualization to develop good mental visualization of programming. (Abstract by author

Teaching and Learning Data Visualization: Ideas and Assignments

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into `pictures', and develop interactive visualizations with, e.g., the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, e.g., Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education

Improving the viability of mental models held by novice programmers

Recent research has found that many novice programmers often hold non-viable mental models of basic programming concepts such as assignment and object reference. This paper proposes a constructivist-based teaching model, integrating a cognitive conflict strategy with program visualization, with the aim of improving novice programmers’ mental models. The results of a preliminary empirical study suggest that, for the relatively straightforward concept of assignment, tight integration of program visualization with a cognitive conflict event that highlights a student’s inappropriate understanding can help improve students’ non-viable mental models. 14 out of 18 participants who held non-viable mental models of the assignment process successfully changed their model to be viable as a result of the proposed teaching model

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material