Integrating computational thinking (CT) into early K-12 education is increasingly recognized as essential for preparing students to navigate a technology-driven world. Digital storytelling, with its capacity to combine narrative expression with programming, offers a promising interdisciplinary strategy for promoting CT. This experience report presents a narrative-centered learning environment that integrates digital storytelling, block-based programming, and hands-on maker activities to foster CT and interdisciplinary learning in upper elementary classrooms. Grounded in a problem-based storyline, the environment engages students in solving real-world-inspired challenges through physical science experimentation and interactive narrative creation. We describe a multi-week classroom implementation with fourth- and fifth-grade students and analyze survey responses and programming artifacts from 41 participants to explore how CT concepts and practices, including sequencing, conditionals, and debugging, emerged in their work. While students demonstrated statistically significant gains in CT, they also encountered challenges related to narrative coherence, science alignment, and conditional logic. We reflect on what did and did not work, offering design insights for educators and designers adopting interdisciplinary, story-driven approaches to computing education.

There is a growing need for 21st-century workers to be digitally literate and to possess computational thinking and collaborative problem-solving skills. Computer-supported collaborative learning (CSCL) focused on computational thinking can guide students toward the co-development of these skills. In this work, we present our approach to integrating virtual and physical learning modalities into InfuseCS, a CSCL environment. InfuseCS uses problem-based learning scenarios to situate upper elementary school students (ages 8 to 11) in a CSCL setting to foster their computational thinking and science knowledge construction as they collaborate to create digital narratives.

Digital storytelling, which combines traditional storytelling with digital tools, has seen growing popularity as a means of creating motivating problem-solving activities in K-12 education. Though an attractive potential solution to integrating language arts skills across topic areas such as computational thinking and science, better understanding of how to structure and support these activities is needed to increase adoption by teachers. Building on prior research on block-based programming for interactive storytelling, we present initial results from a study of 28 narrative programs created by upper elementary students that were collected in both classroom and extracurricular contexts. The narrative programs are evaluated across multiple dimensions to better understand the types of narrative programs being created by the students, characteristics of the students who created the narratives, and what types of support could most benefit the students in their narrative program construction. In addition to analyzing the student-created narrative programs, we also provide recommendations for promising system-generated and instructor-led supports.

One of the most efficient ways for elementary school students to gain exposure to computational thinking is when it is integrated into other disciplinary areas; however, elementary school teachers often lack the necessary resources to do this effectively. By leveraging the motivation force of narrative to engage students and the scaffolding affordances of block-based programming to support students, computationally-rich narrative-centered learning offers promise to address this need. In this work, we review design principles from prior work for engaging elementary students in computational thinking as well as results from initial pilot studies to investigate how computationally-rich narrative-centered learning in the context of science problem solving can support the integration of computational thinking into other disciplinary areas.

Block-based programming environments are widely used by novices who are learning computer science. However, even in block-based coding environments that have been carefully developed to serve novices, students frequently struggle and require additional support. A promising avenue to provide this support is the use of intelligent tutoring systems, which offer adaptive hints to assist learners. In order to provide students with the adaptive hints they need, we must investigate their help-seeking behaviors and identify patterns surrounding their need for support. In this experience report, we examine data collected from 174 college students in an introductory engineering course, who used an intelligent block-based coding environment to learn computer science. These students made more than 1,000 hint requests, which we represent in two-dimensional space along axes of elapsed time and code completeness. Analysis revealed five major clusters of hint requests, which we further characterized through qualitative examination of the coding trajectories that preceded each hint request. We also analyzed how students' incoming knowledge and perceived computer skill were related to their help-seeking behaviors. Students with higher incoming knowledge requested hints when their code was more complete than students with lower incoming knowledge. Students with high perceived computer skill asked for hints when their code was less complete than those with low perceived computer skill. The results presented here provide insight into student help-seeking behavior in computer science education, informing CS educators and system designers on how best to develop support strategies.

Block-based programming environments are widely used in computer science education. However, these environments pose significant challenges for student modeling. Given a series of problem-solving actions taken by students in block-based programming environments, student models need to accurately infer problem-solving students’ programming abilities in real time to enable adaptive feedback and hints that are tailored to students’ abilities. While student models for block-based programming offer the potential to support student-adaptivity, creating student models for these environments is challenging because students can develop a broad range of solutions to a given programming activity. To address these challenges, we introduce a progression trajectory-based student modeling framework for modeling novice student block-based programming across multiple learning activities. Student trajectories utilize a time series representation that employs code analysis to incrementally compare student programs to expert solutions as students undertake block-based programming activities. This paper reports on a study in which progression trajectories were collected from more than 100 undergraduate students engaging in a series of block-based programming activities in an introductory computer science course. Using progression trajectory-based student modeling, we identified three distinct trajectory classes: Early Quitting, High Persistence, and Efficient Completion. Analysis of these trajectories revealed that they exhibit significantly different characteristics with respect to students’ actions and can be used to accurately predict students’ programming behaviors on future programming activities compared to competing baseline models. The findings suggest that progression trajectory-based student models can accurately model students’ block-based programming problem solving and hold potential for informing adaptive support in block-based programming environments.

Recent years have seen an increasing interest in identifying common student misconceptions during introductory programming. In a parallel development, block-based programming environments for novice programmers have grown in popularity, especially in introductory courses. While these environments eliminate many syntax-related errors faced by novice programmers, there has been limited work that investigates the types of misconceptions students might exhibit in these environments. Developing a better understanding of these misconceptions will enable these programming environments and instructors to more effectively tailor feedback to students, such as prompts and hints, when they face challenges. In this paper, we present results from a cluster analysis of student programs from interactions with programming activities in a block-based programming environment for introductory computer science education. Using the interaction data from students' programming activities, we identify three families of student misconceptions and discuss their implications for refinement of the activities as well as design of future activities. We then examine the value of block counts, block sequence counts, and system interaction counts as programming features for clustering block-based programs. These clusters can help researchers identify which students would benefit from feedback or interventions and what kind of feedback provides the most benefit to that particular student.

Recent years have seen a growing recognition of the importance of enabling K-12 students to engage in computational thinking, particularly in elementary grades where students' dispositions toward STEM are developing. Block-based programming has emerged as an effective tool for engaging these novice learners in computational thinking. At the same time, digital storytelling has emerged as a promising avenue for creating motivating problem-solving scenarios that engage students in science investigations. Although block-based programming and digital storytelling are in many ways synergistic, there is a lingering question of how to design block-based languages at an age-appropriate level to enable effective and engaging storytelling. In this work, we review design principles from prior block-based and digital storytelling systems as well as propose the design of block-based programming language features to enable the creation of rich, interactive science narratives by upper elementary students.

Recent years have seen a growing interest in block-based programming environments for computer science education. Although block-based programming offers a gentle introduction to coding for novice programmers, introductory computer science still presents significant challenges, so there is a great need for block-based programming environments to provide students with adaptive support. Predictive student modeling holds significant potential for adaptive support in block-based programming environments because it can identify early on when a student is struggling. However, predictive student models often make a number of simplifying assumptions, such as assuming a normal response distribution or homogeneous student characteristics, which can limit the predictive performance of models. These assumptions, when invalid, can significantly reduce the predictive accuracy of student models.

Recent years have seen a growing recognition of the role that affect plays in learning. Because game-based learning environments elicit a wide range of student affective states, affect-enhanced student modeling for game-based learning holds considerable promise. This paper introduces an affect-enhanced student modeling framework that leverages facial expression tracking for game-based learning. The affect-enhanced student modeling framework was used to generate predictive models of student learning and student engagement for students who interacted with CRYSTAL ISLAND, a game-based learning environment for microbiology education. Findings from the study reveal that the affect-enhanced student models significantly outperform baseline predictive student models that utilize the same gameplay traces but do not use facial expression tracking. The study also found that models based on individual facial action coding units are more effective than composite emotion models. The findings suggest that introducing facial expression tracking can improve the accuracy of student models, both for predicting student learning gains and also for predicting student engagement.

This paper describes a glove with which users enter input by tapping fingertips with the thumb or by rubbing the thumb over the palmar surfaces of the middle and index fingers. The glove has been informally tested as the controller for two semi-autonomous robots in a a 3D simulation environment. A preliminary evaluation of the glove's performance is presented.

In this paper we present a process model for developing usable cross-cultural websites. Compatible with ISO 13407, the process model documents an abstraction of the design process focusing on cultural issues in development. It provides a framework in which a variety of user-based and expert-based techniques for analysis and design are placed within the life-cycle of website development. In developing the model, we relate practical approaches to design with theories and models of culture and discuss the relevance of such theories to the practical design process. In particular we focus on four key concerns: how an audit of local website attractors can inform the design process; the concept of a cultural fingerprint to contrast websites with the cultural needs of local users; the problems associated with user evaluation; and cross-cultural team development. We then show their relation to our process model. We conclude by summarising our contribution to date within the field.

Not so long ago, the practical relevance of cultural issues was widely ignored - if not questioned - in Human–Computer Interaction (HCI) related literature. Initially, the awareness of the importance of human aspects of computer systems shifted the attention of researchers from how computer systems work to how people use these systems in their work. However, the increasing use of such systems in different cultural contexts, and observations that different people think in different ways, put forth the issue of the degree to which the use of such systems is really a matter of culture. Meanwhile, the importance of cultural issues has been evident in many other applied disciplines. Studies have applied concepts of culture from sociology, social psychology, and anthropology. Nevertheless, there are controversies concerning the definition and the use of the concept of culture. When defining culture, researchers often refer to patterns of values, attitudes, and behaviours, which are shared by two or more people. They further point out that culture is socially acquired, and that relationships with other people, relationships with the environment, and assumptions in term of space and language (for example) affect and shape culture, and are themselves affected by the culture.

In this paper we present a process model for developing usable cross-cultural websites. Compatible with ISO 13407, the process model documents an abstraction of the design process focusing on cultural issues in development. It provides a framework in which a variety of user-based and expert-based techniques for analysis and design are placed within the life-cycle of website development. In developing the model, we relate practical approaches to design with theories and models of culture and discuss the relevance of such theories to the practical design process. In particular we focus on four key concerns: how an audit of local website attractors can inform the design process; the concept of a cultural fingerprint to contrast websites with the cultural needs of local users; the problems associated with user evaluation; and cross-cultural team development. We then show their relation to our process model. We conclude by summarising our contribution to date within the field.

Journal Article Prototype evaluation and redesign: structuring the design space through contextual techniques Get access Andy Smith, Andy Smith ∗ aFaculty of Creative Arts and Technologies, University of Luton, Park Square, Luton LU1 3JU, UK ∗Corresponding author. Fax: +44-1582489212 Search for other works by this author on: Oxford Academic Google Scholar Lynne Dunckley Lynne Dunckley bThames Valley University, Wellington Street, Slough SL1 1YG, UK Search for other works by this author on: Oxford Academic Google Scholar Interacting with Computers, Volume 14, Issue 6, December 2002, Pages 821–843, https://doi.org/10.1016/S0953-5438(02)00031-0 Published: 01 December 2002 Article history Received: 11 May 2001 Revision received: 19 November 2001 Accepted: 21 May 2002 Published: 01 December 2002

The Logical User Centred Interface Design (LUCID) method has been shown to provide a development approach which is both user-centred and which, with respect to selected usability criteria, leads to the identification of the optimum interface. Previously published evidence has focused on factors internal to the design of the interface itself. In this paper, the authors show how Taguchi techniques for total quality management, which are integrated within the method, can be extended to analyse external factors such as diversity within the user groups of shared interfaces. Application of the method to global, international and local interfaces is discussed.