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This paper reflects on the process of designing and implementing an update to the "Introduction to Information Technology" course at Bentley University, a business university in New England. Driven by a university-wide curriculum reform initiative and following best practices from digital literacy frameworks and IS model curricula, the new CS 100 course, entitled "Solving Business Problems with Information Technology," shifted emphasis from a personal computing context to exploring small business applications of technology. Students networked with university alumni at "Topics in Tech" presentations, where they learned about career options as information technology professionals. The authors describe the pilot courses that led to proposing a new CS 100 course and reflect its challenges and successes. Challenges included meeting the university's goal of transfer friendliness, scaling from a few pilots to multiple sections, consistency of course delivery, and evaluating assessment. Successes include placing course topics in a business context, introducing current technologies, and providing opportunities for students to learn about career opportunities and experiences from university alumni. The paper also describes how CS 100 aligns with the university's student learning goals for the new curriculum. These include developing future-focused skills, encouraging curiosity and critical analysis, communicating and collaborating in different settings, and demonstrating understanding of issues related to ethical use of technology.

Whereas hackathons are widespread within and outside academia and have been argued to be a valid pedagogical method for teaching interdisciplinarity, no detailed frameworks or methods are available for conceptualizing and organizing educational hackathons, i.e., hackathons dedicated to best achieving pedagogic objectives. This paper is dedicated to introducing EDUCational Hackathons for learning how to solve Interdisciplinary Challenges (EDUCHIC) through: (1) defining the fundamental principles for framing an activity as an EDUCHIC, integrating principles from pedagogical methods, hackathon organization, and interdisciplinarity processes; (2) describing general properties that EDUCHIC possess as a consequence of the interaction of the fundamental principles; (3) developing operational guidelines for streamlining the practical organization of EDUCHIC, including an exhaustive end-to-end process covering all the steps for organizing EDUCHIC and practical frames for carrying the key decisions to be made in this process; and (4) a demonstration of these guidelines through illustrating their application for organizing a concrete EDUCHIC.

In programming problem solving activities, sometimes, students need feedback to progress in the course, being positively affected by the received feedback. This paper presents an overview of the state of the art and practice of the feedback approaches on introductory programming. To this end, we have carried out a systematic literature mapping to understand and discuss the main approaches for providing and evaluating feedback used in the learning of novice programmers in the problem-solving activity. Thus, according to a formal protocol, an automatic search was performed for papers from 2016 to 2021. As a result, 39 studies were selected for the final analysis. As a result, we propose three different categorizations: the main approaches to providing feedback, the main methods used in the evaluation and the main aspects and effects of the evaluated feedback.

Object-oriented programming distinguishes between instance attributes and methods and class attributes and methods, annotated by the "static" modifier. Novices encounter difficulty understanding the means and implications of "static" attributes and methods. The paper has two outcomes: (a) a detailed classification of aspects of understanding "static," and (b) a collection of questions designed to serve as a learning/practice/diagnostic tool to address those aspects. Providing answers requires learners to apply higher-order cognitive skills and, hence, to advance their understanding of the essential meaning of the concept. Each question is analyzed according to three characteristics: (a) the "static" aspects that the question examines according to a detailed classification the paper provides; (b) identification of the question according: to Bloom's revised taxonomy, to the Structure of Observed Learning Outcome (SOLO) taxonomy; and to the problem-solving keywords used in the question's formulation. Several recommendations for teaching are presented.

Instruction in an introductory programming course is typically designed to introduce new concepts and to review and integrate the more recent concepts with what was previously learned in the course. Therefore, most exam questions in an introductory programming course require students to write lines of code that contain syntactic elements corresponding to the programming concepts covered during the instruction. This study investigates the number of concepts involved in the exam problems of an introductory Java programming course. In addition, this study compares how the increase in the number of concepts correlates with the ability of students to write error-free lines of code. The instructional method adopted in this study focuses on providing students with a problem-solving schema and a resultant programming plan that integrates many concepts to meet the problem's goal. Results from this study indicate that as the course progresses through the semester, students, on average, apply appropriate problem-solving schemas and programming plans to produce more error-free lines of code, despite an increase in the concept count in the problems. Furthermore, the exam problems later in the course repeat the application of cluster concepts that have appeared in the past exam. This paper illustrates how programming is a cumulative skill and that repeating and building upon the applications of these concept clusters several times through the course increases the likelihood that students will produce more correct lines of code as the semester progresses.

The second CSEDM data challenge aimed at finding innovative methods to use students' programming traces to model their learning. The main challenge of this task is how to decide which past problems are relevant for predicting performance on a future problem. This paper proposes a set of weighting schemes to address this challenge. Specifically, students' behaviors and performance on past problems were weighted in predicting performance on future problems. The weight of a past problem was proportional to its similarity with the future problem. Problem similarity was quantified in terms of source code, problem prompts, and struggling patterns. In addition, we considered another weighting scheme where past problems were weighted by the order in which students attempted them. Prior studies have used problem similarity and order information in learner modeling, but the proposed weighting schemes are more flexible in capturing problem similarity on various problem properties and weighting various behaviors and performance information on past problems. We systematically investigate the utility of the weighting schemes on performance prediction through two analyses. The first analysis found that the weighting schemes based on source code similarity, struggling pattern similarity, and problem order improved the prediction performance, but the weighting scheme based on problem prompts did not. The second analysis found that the weighting scheme allows a simple and interpretable model, such as logistic regression, to have performance comparable to state-of-the-art deep-learning models. We discussed the implications of the weighting schemes for learner modeling and suggested directions for further improvement.

Nowadays, SPOCs (Small Private Online Courses) have been used as complementary methods to support classroom teaching. SPOCs are courses that apply the usage of MOOCs (Massive Open Online Courses), combining classroom with online education, making them an exciting alternative for contexts such as emergency remote teaching. Although SPOCs have been continuously proposed in the software engineering teaching area, it is crucial to assess their practical applicability via measuring the effectiveness of this resource in the teaching-learning process. In this context, this paper aims to present an experimental evaluation to investigate the applicability of a SPOC in a Verification, Validation, and Software Testing course taught during the period of emergency remote education during the COVID-19 pandemic in Brazil. Therefore, we conducted a controlled experiment comparing alternative teaching through the application of a SPOC with teaching carried out via lectures. The comparison between the teaching methods is made by analyzing the students' performance during the solving of practical activities and essay questions on the content covered. In addition, we used questionnaires to analyze students' motivation during the course. Study results indicate an improvement in both motivation and performance of students participating in SPOC, which corroborates its applicability to the software testing teaching area.

Debugging (or troubleshooting) provides a rich context to foster problem-solving. Yet, while we know much about some problems and strategies that novices face in programming on-screen, we know far less about debugging and troubleshooting in the context of physical computing, where coding issues may overlap with materially embedded problems. In this paper, we study the thought processes novice students employed and the challenges they faced in debugging an electronic textile project with multiple overlapping problems that crossed physical, electronic, and computational domains. We employed a think-aloud protocol to develop an instrumental case study by video-recording 45 minutes of one pair of 9th-grade students debugging and fixing a buggy e-textile project. The problem space included the computational system's programmatic, electronic, and physical spatial aspects, which are more generally reflective of physical computing systems. We found that (1) students' troubleshooting was more recursive and less linear than traditional approaches that usually propose linear, procedural, step-wise activities, and (2) students coordinated their approach across multiple modalities, taking advantage of a distributed set of tools and people in order to tackle a complex set of problems. In the discussion, we address various pedagogical implications for improving teaching about troubleshooting.

This paper introduces a teaching process to develop students' problem-solving and programming efficacy in an introductory computer programming course. The proposed teaching practice provides step-by-step guidelines on using worked-out examples of code to demonstrate the applications of programming concepts. These coding demonstrations explicitly teach the systematic approach and strategies required to develop a programming solution. Each code demonstration is then followed by the instructor assigning similar practice problems to build learners' awareness of the programming process and problem-solving techniques. Every successful attempt of the practice exercise by a student exemplifies their efficacy in applying the programming process and developing solutions using the instructor's strategies. Finally, through regular and structured feedback, the instructor gives learners insight into their performance in completing various steps of the programming process. This paper provides guidelines for creating and using code demonstrations, practice exercises, and rubrics for structured feedback in an introductory programming class. An end-of-course survey was employed to compare students' reported self-efficacy and their actual programming and problem-solving efficacy, based on their completion rates of the practice activities.

Educational technology is shifting toward facilitating personalized learning. Such personalization, however, requires a detailed understanding of students' problem-solving processes. Sequence analysis (SA) is a promising approach to gaining granular insights into student problem solving; however, existing techniques are difficult to interpret because they offer little room for human input in the analysis process. Ultimately, in a learning context, a human stakeholder makes the decisions, so they should be able to drive the analysis process. In this paper, we present a human-in-the-loop approach to SA that uses visualization to allow a stakeholder to better understand both the data and the algorithm. We illustrate the method with a case study in the context of a learning game called Parallel. Results reveal six groups of students organized based on their problem-solving patterns and highlight individual differences within each group. We compare the results to a state-of-the-art method run with the same data and discuss the benefits of our method and the implications of this work.

This paper investigated the effect of block-based programming and unplugged coding teaching activities on fifth graders' computational thinking skills, self-efficacy, and academic performance. The teaching activities were conducted within the scope of the "Problem-Solving and Programming" unit of the Information Technologies and Software (ITS) course. The sample consisted of 82 fifth graders of three public middle schools in the academic year of 2020-2021. Participants were recruited using random sampling. The study adopted an embedded mixed design. The quantitative stage employed a pretest-posttest randomized control group design, while the qualitative staged employed a case study. Quantitative data were collected using the Computational Thinking Self-efficacy Scale (CTSES), the International Informatics and Computational Thinking Activity Task Test (IICTATT), and a Computational Thinking Performance Test (CTPT) developed by the researcher. Qualitative data were collected using a semi-structured interview questionnaire. The quantitative data were analyzed using the Kruskal Wallis H, paired sample t-test, and ANCOVA test on the Statistical Package for Social Sciences(SPSS). The qualitative data were analyzed inductively using MAXQDA. There was no significant difference in CTSES scores between groups. Experimental 2 had higher IICTATT and CTPT scores than Experimental-1 and control groups. The qualitative findings were grouped into seven categories.

There has been much attention given to the use of technology in education; mostly concentrating on physical artifacts of technology to facilitate teaching delivery but little mentioning technology as a cultural resource to organize the learning environment promoting social interaction among students and between teacher and students. This paper thus attempts to derive a model of pedagogy in an IT environment taking social interaction in classroom learning into account and reports a process of validating the model through two observation studies and two questionnaire-based surveys. The first observation study took place in a lecture theatre where a teacher led a class discussion toward developing statistical concepts and showed that all students were attentive and participative. In the second observation study, the students in a computing laboratory were divided into small groups to solve statistical problems collaboratively. The first survey was conducted during the term time to interview students through which they reported their personal perceptions of and attitudes toward the educational use of technology. A questionnaire in the second survey was sent to the students via e-mail after the term time. In the questionnaire, they were asked to compare the experience of working in an IT environment between the classrooms using and not using a model of pedagogy in an IT environment. The students in the surveys reported that teacher facilitation of group learning in the statistics module with technology were different from other statistics modules they took in the same academic year because they appreciated group discussion that promoted social interaction and fostered working relationships, thus leading to construction of knowledge. These four studies show consistent results and evidence in supporting a successful implementation of the model into a statistics classroom practice. More importantly, the observation studies give a detailed account of peer collaboration, mutual assistance, and productive interaction in a process of discussion and the teacher sensitivity to adjust the means of scaffolding assistance that substantiate the survey findings.

Robotics is needed as education keeps up with challenges students are facing in a technological environment. A long-term research project focuses on developing a feasible robotics-based learning environment that enables primary school teachers to introduce computer science education. This paper shows educational robotics combining storytelling to promote computational thinking through the method of Tell, Draw, and Code. The study was conducted via pre-post test, using the Beginners Computational Thinking test (BCTt), with third and fourth graders (N = 40) to determine if the integration of robotics-based storytelling activities enhances computational thinking skills. Results show that an increase in computational thinking is evident after the intervention is implemented. The approach of combining stories, texts, and literature with educational robotics seems to be a promising concept to equip students with the required skills.

Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI to even younger learners. In this theoretical paper, we discuss the design of a game-based learning environment for high school AI education, drawing upon insights gained from a prior cognitive interview study at a STEM focused private high school. We argue that game-based learning is an excellent fit for AI education due to the commonality of problem solving in both game playing and AI.

Purpose: With past studies indicating a lack of skill development in engineering education, there is a need for new pedagogical tools that are application and skill-oriented. Hackathons are widely accepted in the corporate world, in the engineering domain for skill development and recruitment but have not gained acceptance in mainstream engineering education. Very few studies have discussed engineering educator views regarding hackathons and their benefits. This paper intends to study engineering educators' perception of the hackathon and its benefits after participating in it. The findings could support hackathons as a pedagogical tool to develop an industry-oriented and skill-based engineering education. Design/methodology/approach: A mixed methods design was employed where initially qualitative study was conducted. Its findings were used to conduct survey of 162 engineering educators who had participated in a hackathon. A five point Likert scale was employed for data collection. Findings: The findings reveal that engineering educators who participated in the hackathon perceived hackathons to support industry-focused learning, problem-solving and new skill development. They also acknowledged its role in their professional development. The further discussion suggests that engineering educators and institutes may collaborate with industry to design and use hackathons as a teaching tool to develop industry-ready graduates. Research limitations/implications: The study was not designed to study how different aspects of hackathon lead to different benefits derived from participating in it. There is a need to study hackathons as a tool of pedagogy and assessment, focusing on how variables linked to it facilitate, moderate and hinder the learning and assessment process in participants. More in-depth studies need to be conducted to adopt the hackathon as a pedagogy and assessment tool in higher education. Practical implications: The discussion suggests that designing hackathons effectively as a tool for learning and skill development will result in skilled graduates. Engineering educators should adopt hackathons as a pedagogy for their students. Management and policymakers of engineering institutes should consider hackathons as a part of pedagogy for students besides conducting hackathons for educators for their professional development that will be investment in skills helpful at workplace. Originality/value: The notable contribution of this paper is to document perceptions of engineering educators regarding hackathons and their benefits after participating in a hackathon. The paper proposes that hackathons can be introduced in the engineering curriculum as they would offer educators a novel method of teaching and assessment and support engineering graduates in recruitment and making them industry-ready.

This paper explores student emotion and learning experiences fostered by lecturing-style instruction in Real-Analysis problem-centered lessons. We focus on two lessons that were taught by two reputable instructors and involved challenging, mathematically-related problems the students did not understand. Nonetheless, one lesson evoked negative emotional reactions, while the other positive emotional reactions -- a phenomenon we aimed at explaining. The main data comprise the filmed lessons and subsequent stimulated-recall interviews with nine students. The analysis draws on conceptual tools from three perspectives: mathematical discourse, variation theory, and a recently developed construct of "key memorable events" (KMEs) that offers an affective-cognitive lens for investigating the interrelation between teaching and learning. The findings indicate that the positively-perceived lesson contained instances of what we call "heuristic-didactic discourse": a meta-level discourse that presents heuristics monitored from an expert's perspective, yet derived from a student's perspective. Implications for research and practice are drawn.

Over the past years, higher education institutions have been exploring different mechanisms to adapt their learning and teaching practices to increase students' engagement. One of the proposals has been to reuse Massive Online Open Courses (MOOCs) as Small Online Private Courses (SPOCs), or as complementary resources in traditional courses through blended learning practices, such as flipped classroom. However, the integration of online courses as a complement to face-to-face courses poses some challenges. First, students are not used to such blended learning approaches and it is generally difficult for teachers to motivate them to access online resources for the preparation of face-to-face sessions. Second, students are not used to the dynamics of blended learning scenarios, which are less teacher-centered and require their active participation. We propose the use of the mobile application MyMOOCSpace (MMS) to meet these challenges and increase students' motivation and use of learning resources in blended learning courses that use SPOCs as a complement. MMS is a mobile learning application based on gamification mechanisms to promote collaboration and motivation of students in the use of digital resources as a complement to blended learning courses. In this paper, we present the results of a quasi-experiment in a blended course with 294 students that uses a SPOC as a complement, with the aim to assess the effect of MMS on students' motivation and learning resources consumption. In particular, the behavior of two groups of students with the main digital resources of the SPOC (videos and formative assessments) was analyzed: one using the MMS (GTest group), and the other not using MMS (GTrad group). The results suggest that the use of MMS had a positive correlation with the videos consumption, besides increasing student' interaction with assessment exercises in the SPOC.

Programming is an interdisciplinary practice with applications in both mathematics and computer science. Mathematics concerns rigor, abstraction, and generalization. Computer science predominantly concerns efficiency, concreteness, and physicality. This makes programming a medium for problem solving that mediates between mathematics and computer science in intriguing ways. Behind programming practices is computational thinking (CT), a mode of thinking involved in formulating and solving problems so that the solutions could be represented and carried out by computing means. In this paper, CT is seen as a boundary object connecting mathematics and computer science in a school problem-solving context. In particular, we examine and analyse middle school students' work upon engaging in mathematical problem solving-in a programming environment, taking CT as a boundary object embedded in the block-based programming environment, Scratch. The analysis is guided by observing boundary crossing features of CT in the students' artefacts produced in Scratch while solving mathematical problems related to symmetry and arithmetic sequence. The findings of this study open up new dimensions to explore CT as a boundary object in integrated STEM pedagogy.

Videos created with the hands of teachers filmed have been perceived as useful educational resource for students of Physics in undergraduate courses. In previous works, we analyzed the students' perception about educational videos by asking them about their experiences. In this work, we analyze the same facts, but from a learning analytics perspective, by analyzing the interactions that students have with the videos during their learning experience. With this analysis, we obtain how students behave and may compare whether their behavior aligns with the perceptions obtained from previous research. The data analyzed in this work corresponds to the students' interactions with educational videos during 5 semesters in two different courses of Physics within online degrees of Telecommunication and Computer Science. It has been found that the topic taught in the videos has influence in the way videos are used by the students. Regarding the type of content (theory or problem-solving), problem-solving videos are more used by students, although interactions with both videos are similar. This difference differs with previous results based on students' perception. The contribution of the paper is to provide more ground and knowledge about the way the educational videos are consumed in Physics courses. The new knowledge can be used to improve the way videos are incorporated within courses and, therefore, to improve the student learning experiences.

In this paper, I discuss undergraduate students' engagement in basic Python programming while solving combinatorial problems. Students solved tasks that were designed to involve programming, and they were encouraged to engage in activities of prediction and reflection. I provide data from two paired teaching experiments, and I outline how the task design and instructional interventions particularly supported students' combinatorial reasoning. I argue that emergent computational representations and the prompts for prediction and reflection were especially useful in supporting students' reasoning about fundamental combinatorial ideas. I argue that this particular mathematical example informs broader notions of disciplinary reflexivity and representational heterogeneity, providing insight into computational thinking practices in the domain of mathematics. Ultimately, I aim to explore the nature of computing and enumeration, shedding light on why the two disciplines are particularly well-suited to support each other. I conclude with implications and avenues for future research.

Computer Programming Learners usually fail to get approved in introductory courses because solving problems using computers is a complex task. The most important reason for that failure is concerned with motivation; motivation strongly impacts on the learning process. In this paper we discuss how techniques like program animation, and automatic evaluation can be combined to help the teacher in Computer Programming courses. In the article, PEP system will be introduced to explain how it supports teachers in classroom and how it engages students on study sessions outside the classroom. To support that work, students' motivation was studied; to complement that study, a survey involving students attending the first year of Algorithms and Programming course of an Engineering degree was done. It is also presented a tool to analyse surveys, using association rules.

Computational thinking, defined as a way of thinking that can be applied to various fields that require problem-solving skills, has become prevalent in education. Students, i.e., future specialists, have to be prepared for complex thinking competence, necessary for solving business and societal problems, for which a combination of mathematical thinking and computational thinking is essential. The preliminary premise is that there is a correlation between ability in specific mathematical and computational fields. Therefore, this paper aims to highlight the significance of investigating the relations between those fields from linguistic point of view. In order to better understand the relationship between abilities in specific mathematical and computational fields, this paper presents an analysis of a new approach, namely, developing hypotheses for exploring the relationship between metalanguages of different fields of Mathematics and Computer Science. Additionally, the paper describes the first stage of a study on a doctoral level in an attempt to suggest possible statistical analyses suitable for testing hypotheses based on meta-analysis of the current literature. [ABSTRACT FROM AUTHOR]

Computer cooking is a task-related phenomenon where students (end-users) must blindly follow a long list of orders without any connection to the content of the problem, if there is any. Despite its low efficacy, this method is widely used and accepted in informatics both in the learning-teaching process and testing. The National Base Curriculum 2020 in Hungary is in complete accordance with the 'Informatics Reference Framework for Schools', but the course books hardly use the latest results of computer education research. The present paper provides examples of how the results of computer education research can be integrated into teaching-learning materials and classroom practices and discusses the effectiveness and consequences of the different solutions, where tool-centred approaches are compared to problem-focused solutions. [ABSTRACT FROM AUTHOR]

Software education is drawing attention as an education for fostering future talents who will lead the 4th industrial revolution. The purpose of software education for everyone from kinder to adults is not simply to develop programming skills, but to develop "Computational Thinking," a problem-solving ability that effectively solves real-world problems based on computing. Therefore, how to cultivate and evaluate computational thinking is a very important issue. This paper proposed a method of applying a process-based performance evaluation method to evaluate computational thinking ability in the process of solving learners' problems. The developed contents were revised and supplemented through two Delphi surveys by a group of experts consisting of five university professors and five incumbent information teachers majoring in computer science and computer education to verify the effectiveness of the final model. I hope This paper can contribute to the study of evaluating computer thinking ability from the perspective of problem solving. [ABSTRACT FROM AUTHOR]

Background: Skill integration is vital in students' mastery development and is especially prominent in developing code tracing skills which are foundational to programming, an increasingly important area in the current STEM education. However, instructional design to support skill integration in learning technologies has been limited. Objectives: The current work presents the development and empirical evaluation of instructional design targeting students' difficulties in code tracing particularly in integrating component skills in the Trace Table Tutor (T3), an intelligent tutoring system. Methods: Beyond the instructional features of active learning, step‐level support, and individualized problem selection of intelligent tutoring systems (ITS), the instructional design of T3 (e.g., hints, problem types, problem selection) was optimized to target skill integration based on a domain model where integrative skills were represented as combinations of component skills. We conducted an experimental study in a university‐level introductory Python programming course and obtained three findings. Results and Conclusions: First, the instructional features of the ITS technology support effective learning of code tracing, as evidenced by significant learning gains (medium‐to‐large effect sizes). Second, performance data supports the existence of integrative skills beyond component skills. Third, an instructional design focused on integrative skills yields learning benefits beyond a design without such focus, such as improving performance efficiency (medium‐to‐large effect sizes). Major Takeaways: Our work demonstrates the value of designing for skill integration in learning technologies and the effectiveness of the ITS technology for computing education, as well as provides general implications for designing learning technologies to foster robust learning. Lay Description: What is currently known about the subject matter?: Skill integration is vital in students' mastery development, and is especially prominent in developing coding tracing skills.Coding tracing skills are foundational to programming, an increasingly important area in the current STEM education, yet many novice students struggle with code tracing.Instructional design to support skill integration in learning technologies has been limited.One of the most effective technologies for skill mastery, intelligent tutoring systems (ITS), is underused or only used in a partial form in programming education. What our paper adds to this?: We present the development and empirical evaluation of instructional design targeting students' difficulties in code tracing particularly in integrating component skills in an ITS, the Trace Table Tutor.The instructional features of the ITS technology (e.g., active learning, step‐level support, individualized problem selection) support effective learning of code tracing.Students' performance data supports the existence of integrative skills beyond component skills.An instructional design that provides deliberate practice and focused practice on integrative skills yields learning benefits beyond a design without such features. The implications of study findings for practitioners: Designing for skill integration is valuable and should receive more attention in designing learning technologies to foster robust learning.The instructional features of full‐scale ITSs are effective in supporting learning and merit further application in learning technologies in computing education. [ABSTRACT FROM AUTHOR]

This paper presents a game-design workshop built around a digital art installation featuring video games displayed over a real-world skyscraper to stimulate students' interest in computer science and a study testing its short-term effects on improving middle school students' computational thinking (CT) skills and attitudes towards computing. Following a STEAM approach, the workshop aimed to engage participants in age-appropriate activities that focus on CT skills through the lens of creating their own game. A web-based game design interface that allows students to code and play games as simulated on a skyscraper was developed to support the workshop's core activities. The web environment also featured step-by-step tutorials and fully functional games to promote the accessibility of the learning materials for a diverse body of students and educators around the globe. The results of the study indicated that the workshop helped students improve their CT skills and differentially influence their attitudes towards computing. In particular, the workshop experience led students from underserved community districts to lower their attitude ratings, whereas the reverse pattern was observed for students from more affluent districts. The workshop reportedly informed students' perception of computing as a profession and their appreciation of the analytical effort required for developing functional games. Qualitative analysis of artifact-based interviews indicated that students could begin to make abstractions and devise algorithms by associating variables through conditional statements while solving problems related to game development. Interview analysis also revealed that students took pride in the effort that they made during the workshop. [ABSTRACT FROM AUTHOR]

Programming is acknowledged widely as a cornerstone skill in Computer Science education. Despite significant efforts to refine teaching methodologies, a segment of students is still at risk of failing programming courses. It is crucial to identify potentially struggling students at risk of underperforming or academic failure. This study explores the predictive potential of students' problem-solving skills through dynamic, domain-independent, complex problem-solving assessment. To evaluate the predictive potential of complex problem-solving empirically, a case study with 122 participants was conducted in the undergraduate Introductory Programming Course at the University of Maribor, Slovenia. A latent variable approach was employed to examine the associations. The study results showed that complex problem-solving has a strong positive effect on performance in Introductory Programming Courses. According to the results of structural equation modeling, 64% of the variance in programming performance is explained by complex problem-solving ability. Our findings indicate that complex problem-solving performance could serve as a significant, cognitive, dynamic predictor, applicable to the Introductory Programming Course. Moreover, we present evidence that the demonstrated approach could also be used to predict success in the broader computing education community, including K-12, and the wider education landscape. Apart from predictive potential, our results suggest that valid and reliable instruments for assessing complex problem-solving could also be used for assessing general-purpose, domain-independent problem-solving skills in computing education. Likewise, the results confirmed the positive effect of previous programming experience on programming performance. On the other hand, there was no significant direct effect of performance in High School mathematics on Introductory Programming. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) has garnered significant interest in both computer science and education sciences as it delineates a set of skills that emerge during the problem-solving process. Consequently, numerous assessment instruments aimed at measuring CT have been developed in the recent years. However, a scarce part of the existing CT measurement instruments has been dedicated to early school ages, and few have undergone rigorous validation or reliability testing. Therefore, this work introduces a new instrument for measuring CT in the early grades of elementary education: the Computational Thinking Test for Children (CTTC). To this end, in this work, we provide the design and validation of the CTTC, which is constructed around spatial, sequential, and logical thinking and encompasses abstraction, decomposition, pattern recognition, and coding items organized in five question blocks. The validation and standardization process employs the Kuder–Richardson statistic (KR-20) and expert judgment using V-Aiken for consistency. Additionally, item difficulty indices were utilized to gauge the difficulty level of each question in the CTTC. The study concludes that the CTTC demonstrates consistency and suitability for children in the first cycle of primary education (encompassing the first to third grades). [ABSTRACT FROM AUTHOR]

Distance education programs have become the preferred option for most higher education institutions to continue teaching during the COVID-19 pandemic, but the effectiveness of some online courses, especially those engineering courses with experimentation activities, remains disputed. The main challenge is fostering collaborative problem solving skills for novice students as online collaboration increases their cognitive load. This research thus tapped into novice engineering students' cognitive load to develop a more granular, multimodal view of how cognitive load influences student performance in collaborative problem solving during virtual experimentation activities. The findings of this research provided significant implications for the future design and implementation of virtual laboratories in computer science engineering education. [ABSTRACT FROM AUTHOR]

In order to teach Computational Thinking (CT) skills to young students, Block-Based Programming Environments (BBPEs) are integrated into secondary school computer science (CS) education curricula. As a CT skill, abstraction is one of the prominent skills, which is difficult to enhance and measure. Researchers developed some scales for measuring abstraction in BBPEs; however, it is still quite difficult to measure abstraction and understand students' abstraction behaviors. The aim of this study is to suggest tasks that could help enhance students' abstraction skills while teaching CT via block-based programming. In addition, a rubric to score the students' abstraction behaviors in the problem solving process was created and validated. A framework with regard to the definitions of abstraction skill was adopted and the way to isolate it from other CT-skills was proposed. As a result, pattern recognition, generalizing, decomposition, focusing and eliminating were defined as indicators of abstraction in the problem solving process via BBPEs. The study also informed computer science educators about the relations between teaching CT via BBPEs, affordances of BBPEs and nature of abstraction. [ABSTRACT FROM AUTHOR]