Learning during COVID-19 pandemic: A systematic literature review

The COVID-19 pandemic has transformed education worldwide. Learning that is usually done offline has turned into online learning to avoid the spread of the COVID-19 virus. The purpose of writing this article is to describe the latest and updated learning conditions during the COVID-19 pandemic. What kind of learning was carried out during the COVID-19 pandemic, was the learning effective, how was the learning outcome, and the challenges faced. The systematic literature review is used to find answers to this article's purpose by synthesizing 53 articles selected according to the criteria. The synthesis results found that online learning was carried out using video conference as a substitute for face-to-face meetings, discussions, exams, and learning feedback using supporting applications. The internet was the direct support. Most of the learning shows significant results. Learning outcomes cannot be concluded whether it is good or not. There are many challenges during learning. It indicates that the world of education is not fully ready to transform from offline learning to online learning. A standard platform for online learning and rules of learning is required to minimize negative impacts and pay attention to the socio-emotional aspect

Review of Measurements Used in Computing Education Research and Suggestions for Increasing Standardization

The variables that researchers measure and how they measure them are central in any area of research. Which research questions can be asked and how they are answered depends on measurement. This paper describes a systematic review of the literature in computing education research to summarize the commonly used variables and measurements in 197 papers and to compare them to best practices in measurement for human-subjects research. Characteristics of the literature that are examined in the review include variables measured (including learner characteristics), measurements used, and type of data analysis. The review illuminates common practices related to each of these characteristics and their interactions with other characteristics. The paper lists standardized measurements that were used in the literature and highlights commonly used variables for which no standardized measures exist. To conclude, this review compares common practice in computing education to best practices in human-subjects research to make recommendations for increasing rigor

Tools and Environments

Teaching and learning how to build software are central aspects of computing education, and the tools which we use to support this are themselves a focus of research and innovation. This chapter considers tools designed or predominately used for education; from software development environments to automatic assessment tools, visualization, and educational games platforms. It looks at not just the history and state-of-the-art of these tools, but also at the challenges and opportunities in researching with and about them

Programmeerimise sissejuhatava kursuse õppijate teadmisi ja oskusi mõõtev test

Programmeerimise õpetamisel tuleb pöörata tähelepanu õppijate teadmiste ja oskuste taseme hindamisele nii õppe alguses kui ka selle lõppemisel. Seda saab teha testi vormis. Magistritöö eesmärgiks on luua valideeritud test, mille abil saab hinnata õppijate teadmisi ja oskusi programmeerimiskursuse “Tehnoloogia tarbijast loojaks” alguses ja/või lõpus. Töö esimeses peatükis on toodud ülevaade Venemaal ja USAs kasutusel olevatest valideeritud testidest. Töö teises peatükis on kirjeldatud testi loomise protseduuri ning piloteerimise protsessi. Töö tulemusena loodud test piloteeriti kursuse “Tehnoloogia tarbijast loojaks” arvestustööks ettevalmistava testina. Võib öelda, et testi on võimalik kasutada nimetatud kursuse õppijate teadmiste ja oskuste hindamisel

Self-Regulation and Academic Motivation as Predictors of Academic Achievement of Undergraduate Students in an Online Learning Environment

Problem Online learning is a form of distance education that occurs via the Internet (Adams, 2004; Carliner, 1999). The success of online learning depends on student-related factors such as acceptance, willingness, and motivation (Almaiah et al., 2019; Almaiah et al., 2020). When online learning systems are not utilized, students cannot realize the concomitant benefit of improved performance. Online learning has many positives, but it does present a problem when it comes to academic success. Motivation and self-regulation are two of the key factors for successful online learning given that students are subject to minimal supervision or guidance from teachers in an online environment. Lack of motivation and low levels of self-regulation can decrease students’ academic outcomes. It is, therefore, important to understand to what extent self-regulation and motivation impact academic success in an online learning environment, to better develop strategies to improve motivation, enhance self-regulation, and address the shortcomings they present to online learning. The focus of this study is the relationship between students’ self-regulation, academic motivation, and academic achievement in an online learning environment (Alafghani & Purwandari, 2019; Bandura, 1991; Panadero, 2017; Yusuf, 2011). Method A non-experimental quantitative research design was used to investigate the relationship between self-regulation, academic motivation, and academic achievement of undergraduate students in an online learning environment. QuestionPro hosted the survey. The data were collected using self-report questionnaires, and the sample population consisted of 300 undergraduate students taking online courses in the United States. The Online Self-Regulated Learning Questionnaire (OSLQ) and Academic Motivation Scale (AMS) were used to measure self-regulation and academic motivation, respectively. Structural Equation Modeling (SEM) was used to analyze the data, and Multigroup Structural Equation Modeling (MSEM) was used to understand gender differences between the variables. To achieve a valid interpretation of data, the data was collected, screened, and analyzed using SPSS and AMOS software. Results In the current study, 41% of participants were seniors, 60% were taking four or fewer online courses, and 68% had a GPA above 3.0. Results of this study indicated that the self-regulation of undergraduate students in an online learning environment were at moderate levels. The subscale reported a high level of help-seeking and environment structuring. Task strategies, time management, goal setting, and self-evaluations were at a moderate level. Academic motivation, intrinsic motivation, and extrinsic motivation scales and subscales were at moderate levels. However, extrinsic motivation identified (ExMD) reported a high level. Amotivation was at the lowest level of academic motivation. There was no correlation between self-regulation and academic motivation in the academic achievement of undergraduate students in an online learning environment. However, self-regulation and academic motivation showed a positive and statistically significant correlation and the final model accounted for approximately 3% of the variance of academic achievement. The analysis revealed no gender differences between male and female undergraduate students in an online learning environment regarding their self-regulation, academic motivation, and academic achievement. Conclusions The findings support the current research and forms the basis for future research studies of students’ academic motivation, self-regulation, and academic performance in the online environment. Future research should conduct longitudinal studies to track students’ self-regulation skills, motivation, and academic achievement in an online learning environment to better understand their development and impact on academic outcomes. Such research should also investigate the role of individual differences, such as personality traits and prior academic achievement, in the relationship between self-regulation, academic motivation, and academic achievement in online learning environments

Code Puzzle Completion Problems in Support of Learning Programming Independently

Middle school children often lack access to formal educational opportunities to learn computer programming. One way to help these children may be to provide tools that enable them to learn programming on their own independently. However, in order for these tools to be effective they must help learners acquire programming knowledge and also be motivating in independent contexts. I explore the design space of using motivating code puzzles with a method known to support independent learning: completion problems. Through this exploration, I developed code puzzle completion problems and an introductory curriculum introducing novice programmers to basic programming constructs. Through several evaluations, I demonstrate that code puzzle completion problems can motivate learners to acquire new programming knowledge independently. Specifically, I found that code puzzle completion problems are more effective and efficient for learning programming constructs independently compared to tutorials. Puzzle users performed 33% better on transfer tasks compared to tutorial users, while taking 21% less time to complete the learning materials. Additionally, I present evidence that children are motivated to choose to use the code puzzles because they find the experience enjoyable, challenging, and valuable towards developing their programming skills. Given the choice between using tutorials and puzzles, only 10% of participants opted to use more tutorials than puzzles. Further, 80% of participants also stated a preference towards the puzzles because they simply enjoyed the experience of using puzzles more than the tutorials. The results suggest that code puzzle completion problems are a promising approach for motivating and supporting independent learning of programming

Development and Application of a Rasch Model Measure of Student Competency in University Introductory Computer Programming

University computer programming instruction nomenclature commonly uses the term Computer Science 1 (CS1) to describe introductory units of study. Success in CS1 is important as a pre-requisite for further study in programming and related disciplines. It is important to measure student progress and the antecedent influences. This study applied the Rasch Model and Messick’s Unified Theory of Validity to construct an interval level measure of CS1 competency with demonstrable suitability for this purpose

Considering primary school programming education using a comprehension-first approach

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