Through (Tracking) Their Eyes: Abstraction and Complexity in Program Comprehension

Previous studies on writing and understanding programs presented evidence that programmers beyond a novice stage utilize plans or plan-like structures. Other studies on code composition showed that learners have difficulties with writing, reading, and debugging code where interacting plans are merged into a short piece of code. In this article, we focus on the question of how different code-composition strategies and the familiarity with code affect program comprehension on a more abstract, i.e., algorithmic level. Using an eye-tracking setup, we explored how advanced students comprehend programs and their underlying algorithms written in either a merged or abutted (sequenced) composition of code blocks of varying familiarity. The effects of familiarity and code composition were studied both isolated and in combination. Our analysis of the quantitative data adds to our understanding of the behavior reported in previous studies and the effects of plans and their composition on the programs’ difficulty. Using this data along with retrospective interviews, we analyze students’ reading patterns and provide support that subjects were able to form mental models of program execution during task performance. Furthermore, our results suggest that subjects are able to retrieve and create schemata when the program is composed of familiar templates, which may improve their performance; we found indicators for a higher element-interactivity for programs with a merged code composition compared to abutted code composition.

The Five-Factor Perceived Shared Mental Model Scale: A Consolidation of Items Across the Contemporary Literature

Literature on Shared Mental Models (SMMs) has been burgeoning in recent years and this has provided increasingly detailed insight and evidence into the importance of SMMs within specific contexts. However, because past research predominantly focused on SMM structure as measured by diverse, context-dependent measures, a consolidated multi-dimensional measure of perceived SMMs that can be used across diverse team contexts is currently lacking. Furthermore, different conceptualizations of the dimensionality of SMMs exist, which further impedes the comparison between studies. These key limitations might hinder future development in the SMM literature. We argue that the field of SMMs has now matured enough that it is possible to take a deductive approach and evaluate the prior studies in order to refine the key SMMs dimensions, operationalizations, and measurement. Hence, we take a three-stage approach to consolidate existing literature scale-based measures of SMMs, using four samples. Ultimately, this leads to a 20-item five-dimensional scale (i.e., equipment, execution, interaction, composition, and temporal SMMs) – the Five Factor Perceived Shared Mental Model Scale (5-PSMMS). Our scale provides scholars with a tool which enables the measurement, and comparison, of SMMs across diverse team contexts. It offers practitioners the option to more straightforwardly assess perceived SMMs in their teams, allowing the identification of challenges in their teams and facilitating the design of appropriate interventions.

Mapping mental models of science communication: How academics in Germany, Austria and Switzerland understand and practice science communication

The mental models that individual scholars have of science communication – how it works, what it is supposed to achieve and so on – shape the way these academics actually communicate to the public. But these mental models, and their prevalence among scholars, have rarely been analysed. Drawing on a large-scale, representative web survey of academics at universities in Germany, Austria and Switzerland ( n = 15,778) from 2020, we identify three mental models that are prevalent among scholars, and that correspond to conceptual models found in science communication theory: ‘Public Understanding of Science’, ‘Public Engagement with Science’ and ‘Strategic Science Communication’. The results suggest that the ‘Strategic Science Communication’ model is particularly prevalent among academics in precarious employment and female scholars. Extrinsically motivated academics, that is, those under pressure to win grants, also seem to use science communication more strategically. The ‘Public Engagement’ model is prevalent among older and female scholars, while ‘Public Understanding’ is particularly prevalent among scholars who find their work especially meaningful. Findings also reveal that academics’ mental models largely align with the way they practice science communication.

The Importance of the Journey Map in the Design of a Playful Interactive-Interface

This article presents the importance of using the customer journey map (CJM) as a tool for managing new information technologies. The case study presents the use of the CJM to indicate the experiences that people have when using a playful interactive-interface, where the causal relationship between the brightness and the distance of the stars is visualized. The CJM is the result of a quantitative and qualitative study about user experience (UX) or UX research. Where you can use: 1) observation techniques, 2) interviews, 3) questionnaires, 4) a review of the domain literature about the phenomenon, archetypal descriptions, 5) diagrams of mental models; also known as thought schemes, or cognitive maps, among others. This research focuses the development of the interface in the design of archetypal profiles and mental models to generate the CJM, in the domain of the specific phenomenon.

Exposing Mental Models to Accelerate Distance Learning Environments

This chapter forms one part in a series of chapters offering recommendations to design effective distance and blended learning. The COVID-19 pandemic has been an unintended catalyst for change in schools. Due to the closing of school buildings in the spring of 2020, school districts were forced to quickly transition to distance learning or blended learning. Even before COVID-19, many districts failed to successfully and systematically implement the new knowledge and skills acquired in these sessions. So, the question is, why has blended and distance learning practices continued to function in pockets throughout a school district and not systematically throughout the school organization? One can argue that full implementation was impeded by the lack of budgetary resources and infrastructure or unaligned curriculum, instruction, and assessments.

Crisis behaviors as drivers of value co-creation transformation

Purpose The paper aims to conceptualize how behavioral shifts in times of crisis drive the transformation of value co-creation. Design/methodology/approach Referencing two empirical contexts, the paper explores how digital service platforms facilitate changes in actors’ mental models and institutional arrangements (legal, social, technological) that drive transformation of value co-creation in service ecosystems. Findings The proposed conceptual framework contributes to existing research by identifying micro-level changes in actors’ mental models and macro-level changes in institutional arrangements enabled by digital service platforms in service ecosystems. In particular, the framework identifies motivation, agility and resistance as moderators of behavioral shifts in times of crisis. This account offers a finer-grained theorization of the moderating factors and underlying mechanisms of service ecosystem transformation but does not extend to the ensuing “new normal.” Practical implications The proposed framework indicates how digital platforms support shifts in actors’ behavior and contribute to the transformation of value co-creation. While the enablers are situation-specific and may therefore vary according to the prevailing conditions, the actor-related concepts advanced here seem likely to remain relevant when analyzing the transformation of value co-creation in other crisis situations. Originality/value The new conceptual framework advanced here clarifies how behavioral shifts during a crisis drive the transformation of value co-creation and suggests directions for future research.