Computational Thinking and Literacy

Today’s students will enter a workforce that is powerfully shaped by computing. To be successful in a changing economy, students must learn to think algorithmically and computationally, to solve problems with varying levels of abstraction. These computational thinking skills have become so integrated into social function as to represent fundamental literacies. However, computer science has not been widely taught in K-12 schools. Efforts to create computer science standards and frameworks have yet to make their way into mandated course requirements. Despite a plethora of research on digital literacies, research on the role of computational thinking in the literature is sparse. This conceptual paper proposes a three dimensional framework for exploring the relationship between computational thinking and literacy through: 1) situating computational thinking in the literature as a literacy; 2) outlining mechanisms by which students’ existing literacy skills can be leveraged to foster computational thinking; and 3) elaborating ways in which computational thinking skills facilitate literacy development

Programming music with Sonic Pi promotes positive attitudes for beginners

Publisher Copyright: © 2021 The AuthorProgramming is often misaligned with beginner students' interests and viewed as difficult. However, most students and teachers are not aware that it is possible to utilise domain-specific programming languages that combine programming with other domains like music making. Sonic Pi is one free domain-specific programming platform that enables beginners to code music, which has been designed for and used in schools since its first release in 2012. However, there is a lack of academic research on the Sonic Pi platform about the extent it may affect beginner student attitudes towards programming in a school context. The aim of this study was to investigate the extent Sonic Pi may help to promote positive attitudes towards programming. A mixed-methods case study was developed and trialled in school time with a middle school class, which measured student attitudes with the three subscales of enjoyment, importance, and anxiety. Overall, the results confirmed an alternative hypothesis that all students’ subscales for programming attitude increased significantly. While these findings are not generalisable due to its limited scope, they are very positive to inform the design and use of platforms like Sonic Pi in comparison to similar music coding platforms like EarSketch and TunePad.Peer reviewe

Exploring Trends in Middle School Students\u27 Computational Thinking in the Online Scratch Community: A Pilot Study

Teaching computational thinking has been a focus of recent efforts to broaden the reach of computer science (CS) education for today’s students who live and work in a world that is heavily influenced by computing principles. Computational thinking (CT) essentially means thinking like a computer scientist by using principles and concepts learned in CS as part of our daily lives. Not only is CT essential for the development of computer applications, but it can also be used to support problem solving across all disciplines. Computational thinking involves solving problems by drawing from skills fundamental to CS such as decomposition, pattern recognition, abstraction, and algorithm design. The present study examined how Dr. Scratch, a CT assessment tool, functions as an assessment for computational thinking. This study compared strengths and weaknesses of the CT skills of 360 seventh- and eighth-grade students who were engaged in a Scratch programming environment through the use of Dr. Scratch. The data were collected from a publicly available dataset available on the Scratch website. The Mann-Whitney U analysis revealed that there were specific similarities and differences between the seventh- and eighth-grade CT skills. The findings also highlight affordances and constraints of Dr. Scratch as a CT tool and address the challenges of analyzing Scratch projects from young Scratch learners. Recommendations are offered to researchers and educators about how they might use Scratch data to help improve students’ CT skills

Factors influencing the learning of introductory computer programing at the Durban University of Technology.

Masters Degree. University of KwaZulu-Natal, Durban.Computer programming is an extremely difficult skill to master for students who are novice computer programmers. The preceding assertion is based on reports of high failure rates in introductory computer programming courses offered by tertiary education institutions. This is not just a South African problem but a number of cross-institutional and multi-national studies show that the problem is well known and is common (Grover et al., 2016). The current study investigated the factors influencing the learning of introductory computer programing at Durban University of Technology (DUT). The objectives of the study were to understand the influence of previous experience on students’ learning of introductory computer programming as well as to understand the influence of self-efficacy on students’ learning of introductory computer programming. The study also focused on understanding the influence of the ‘mental model ‘representation of the problem domain on students’ learning of introductory computer programming, and to understand the influence of the ‘mental model’ representation of the problem domain on students’ self-efficacy in the learning of introductory computer programming. The study adopted the quantitative research method to investigate the subject matter. This study embraced a survey research strategy and data collection carried out was over a short period. The study used simple random sampling to select 200 respondents at DUT. Data were collected using questionnaires. Data quality control was ensured by conducting a reliability and validity test on the data collection instrument used in this study. Ethical approval for the study was obtained from DUT. The quantitative data collected were analyzed using the SPSS, version 25.0. The study utilized statistics such as frequency, descriptive (mean and standard deviation) and inferential statistics (Cronbach’s alpha and Spearman correlation). The overall findings from the study suggested that the self-efficacy level of the research participants was high. The results of the study revealed that there was a moderate positive relationship between self-efficacy and computer programming. Furthermore, it found was that the mental model adopted by students when solving computer programming problems positively influences student performance in computer programming. An outcome of the study is the recommendation that the teaching and learning of computer programming should focus on language structure and the correct mental interpretation of the problem domain so that students could improve their performance

Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

This paper presents a Support Vector Machine predictive model to determine if prior programming knowledge and completion of in-class and take home formative assessment tasks might be suitable predictors of examination performance. Student data from the academic years 2012 - 2016 for an introductory programming course was captured via ViLLE e-learning tool for analysis. The results revealed that student prior programming knowledge and assessment scores captured in a predictive model, is a good fit of the data. However, while overall success of the model is significant, predictions on identifying at-risk students is neither high nor low and that persuaded us to include two more research questions. However, our preliminary post analysis on these test results show that on average students who secured less than 70% in formative assessment scores with little or basic prior programming knowledge in programming may fail in the final programming exam and increase the prediction accuracy in identifying at-risk students from 46% to nearly 63%. Hence, these results provide immediate information for programming course instructors and students to enhance teaching and learning process.</p

Computer Science at Community Colleges: Attitudes and Trends

This study aimed to understand the identity and attitude of students enrolled in computer science (CS) or programming-related course at community colleges nationwide. This study quantitatively evaluation data for estimating the relationships between students’ identity and attitudes toward computer science with prior programming experience and other demographic factors. I distributed the survey to community college faculty of computer science programs nationwide. Questions for this study were adapted from the Computing Attitude Survey developed by Weibe, Williams, Yang, & Miller (2003). Using two robust quantitative statistical methodologies, I investigated the correlations and predictability of previous programming experience, gender, race, and age with participants\u27 attitudes toward computer science. This study drew its inspiration from prior works of Dorn and Tew (2015) and Chen, Haduong, Brennan, Sonnert, and Sadler (2018), whose studies looked at previous experiences in programming with a favorable attitude toward computer science. The primary independent variable was a students’ prior programming experience. Under evaluation, the dependent variables were students\u27 programming experience and demographic characteristics such as race, gender, and age. This investigation showed a significant association between programming experience and attitude toward computer science. Among the demographic variables evaluated, students\u27 racial identity was the only factor found highly correlated with attitudes toward computer science. Future work will consider the association between participants\u27 accumulated college credit hours and specific programming language effects on computer science attitudes

Computational Thinking in Education: Where does it fit? A systematic literary review

Computational Thinking (CT) has been described as an essential skill which everyone should learn and can therefore include in their skill set. Seymour Papert is credited as concretising Computational Thinking in 1980 but since Wing popularised the term in 2006 and brought it to the international community's attention, more and more research has been conducted on CT in education. The aim of this systematic literary review is to give educators and education researchers an overview of what work has been carried out in the domain, as well as potential gaps and opportunities that still exist. Overall it was found in this review that, although there is a lot of work currently being done around the world in many different educational contexts, the work relating to CT is still in its infancy. Along with the need to create an agreed-upon definition of CT lots of countries are still in the process of, or have not yet started, introducing CT into curriculums in all levels of education. It was also found that Computer Science/Computing, which could be the most obvious place to teach CT, has yet to become a mainstream subject in some countries, although this is improving. Of encouragement to educators is the wealth of tools and resources being developed to help teach CT as well as more and more work relating to curriculum development. For those teachers looking to incorporate CT into their schools or classes then there are bountiful options which include programming, hands-on exercises and more. The need for more detailed lesson plans and curriculum structure however, is something that could be of benefit to teachers

Predictive models as early warning systems for student academic performance in introductory programming

Computer programming is fundamental to Computer Science and IT curricula. At the novice level it covers programming concepts that are essential for subsequent advanced programming courses. However, introductory programming courses are among the most challenging courses for novices and high failure and attrition rates continue even as computer science education has seen improvements in pedagogy. Consequently, the quest to identify factors that affect student learning and academic performance in introductory computer programming courses has been a long-standing activity. Specifically, weak novice learners of programming need to be identified and assisted early in the semester in order to alleviate any potential risk of failing or withdrawing from their course. Hence, it is essential to identify at-risk programming students early, in order to plan (early) interventions. The goal of this thesis was to develop a validated, predictive model(s) with suitable predictors of student academic performance in introductory programming courses. The proposed model utilises the Naïve Bayes classification machine learning algorithm to analyse student performance data, based on the principle of parsimony. Furthermore, an additional objective was to propose this validated predictive model as an early warning system (EWS), to predict at-risk students early in the semester and, in turn, to potentially inform instructors (and students) for early interventions. We obtained data from two introductory programming courses in our study to develop and test the predictive models. The models were built with student presage and in progress-data for which instructors may easily collect or access despite the nature of pedagogy of educational settings. In addition, our work analysed the predictability of selected data sources and looked for the combination of predictors, which yields the highest prediction accuracy to predict student academic performance. The prediction accuracies of the models were computed by using confusion matrix data including overall model prediction accuracy, prediction accuracy sensitivity and specificity, balanced accuracy and the area under the ROC curve (AUC) score for generalisation. On average, the models developed with formative assessment tasks, which were partially assisted by the instructor in the classroom, returned higher at-risk prediction accuracies than the models developed with take-home assessment task only as predictors. The unknown data test results of this study showed that it is possible to predict 83% of students that need support as early as Week 3 in a 12-week introductory programming course. The ensemble method-based results suggest that it is possible to improve overall at-risk prediction performance with low false positives and to incorporate this in early warning systems to identify students that need support, in order to provide early intervention before they reach critical stages (at-risk of failing). The proposed model(s) of this study were developed on the basis of the principle of parsimony as well as previous research findings, which accounted for variations in academic settings, such as academic environment, and student demography. The predictive model could potentially provide early warning indicators to facilitate early warning intervention strategies for at-risk students in programming that allow for early interventions. The main contribution of this thesis is a model that may be applied to other programming and non-programming courses, which have both continuous formative and a final exam summative assessment, to predict final student performance early in the semester.Ohjelmointi on informaatioteknologian ja tietojenkäsittelytieteen opinto-ohjelmien olennainen osa. Aloittelijatasolla opetus kattaa jatkokurssien kannalta keskeisiä ohjelmoinnin käsitteitä. Tästä huolimatta ohjelmoinnin peruskurssit ovat eräitä haasteellisimmista kursseista aloittelijoille. Korkea keskeyttämisprosentti ja opiskelijoiden asteittainen pois jättäytyminen ovat vieläkin tunnusomaisia piirteitä näille kursseille, vaikka ohjelmoinnin opetuksen pedagogiikka onkin kehittynyt. Näin ollen vaikuttavia syitä opiskelijoiden heikkoon suoriutumiseen on etsitty jo pitkään. Erityisesti heikot, aloittelevat ohjelmoijat tulisi tunnistaa mahdollisimman pian, jotta heille voitaisiin tarjota tukea ja pienentää opiskelijan riskiä epäonnistua kurssin läpäimisessä ja riskiä jättää kurssi kesken. Heikkojen opiskelijoiden tunnistaminen on tärkeää, jotta voidaan suunnitella aikainen väliintulo. Tämän väitöskirjatyön tarkoituksena oli kehittää todennettu, ennustava malli tai malleja sopivilla ennnustusfunktioilla koskien opiskelijan akateemista suoriutumista ohjelmoinnin peruskursseilla. Kehitetty malli käyttää koneoppivaa naiivia bayesilaista luokittelualgoritmia analysoimaan opiskelijoiden suoriutumisesta kertynyttä aineistoa. Lähestymistapa perustuu yksinkertaisimpien mahdollisten selittävien mallien periaatteeseen. Lisäksi, tavoitteena oli ehdottaa tätä validoitua ennustavaa mallia varhaiseksi varoitusjärjestelmäksi, jolla ennustetaan putoamisvaarassa olevat opiskelijat opintojakson alkuvaiheessa sekä informoidaan ohjaajia (ja opiskelijaa) aikaisen väliintulon tarpeellisuudesta. Keräsimme aineistoa kahdelta ohjelmoinnin peruskurssilta, jonka pohjalta ennustavaa mallia kehitettiin ja testattiin. Mallit on rakennettu opiskelijoiden ennakkotietojen ja kurssin kestäessä kerättyjen suoriutumistietojen perusteella, joita ohjaajat voivat helposti kerätä tai joihin he voivat päästä käsiksi oppilaitoksesta tai muusta ympäristöstä huolimatta. Lisäksi väitöskirjatyö analysoi valittujen datalähteiden ennustettavuutta ja sitä, mitkä mallien muuttujista ja niiden kombinaatioista tuottivat kannaltamme korkeimman ennustetarkkuuden opiskelijoiden akateemisessa suoriutumisessa. Mallien ennustusten tarkkuuksia laskettiin käyttämällä sekaannusmatriisia, josta saadaan laskettua ennusteen tarkkuus, ennusteen spesifisyys, sensitiivisyys, tasapainotettu tarkkuus sekä luokitteluvastekäyriä (receiver operating characteristics (ROC)) ja näiden luokitteluvastepinta-ala (area under curve (AUC)) Mallit, jotka kehitettiin formatiivisilla tehtävillä, ja joissa ohjaaja saattoi osittain auttaa luokkahuonetilanteessa, antoivat keskimäärin tarkemman ennustuksen putoamisvaarassa olevista opiskelijoista kuin mallit, joissa käytettiin kotiin vietäviä tehtäviä ainoina ennusteina. Tuntemattomalla testiaineistolla tehdyt mallinnukset osoittavat, että voimme tunnistaa jo 3. viikon kohdalla 83% niistä opiskelijoista, jotka tarvitsevat lisätukea 12 viikkoa kestävällä ohjelmoinnin kurssilla. Tulosten perusteella vaikuttaisi, että yhdistämällä metodeja voidaan saavuttaa parempi yleinen ennustettavuus putoamisvaarassa olevien opiskelijoiden suhteen pienemmällä määrällä väärin luokiteltuja epätositapauksia. Tulokset viittaavat myös siihen, että on mahdollista sisällyttää yhdistelmämalli varoitusjärjestelmiin, jotta voidaan tunnistaa avuntarpeessa olevia opiskelijoita ja tarjota täten varhaisessa vaiheessa tukea ennen kuin on liian myöhäistä. Tässä tutkimuksessa esitellyt mallit on kehitetty nojautuen yksinkertaisimman selittävän mallin periaatteeseen ja myös aiempiin tutkimustuloksiin, joissa huomioidaan erilaiset akateemiset ympäristöt ja opiskelijoiden tausta. Ennustava malli voi tarjota indikaattoreita, jotka voivat mahdollisesti toimia pohjana väliintulostrategioihin kurssilta putoamisvaarassa olevien opiskelijoiden tukemiseksi. Tämän tutkimuksen keskeisin anti on malli, jolla opiskelijoiden suoriutumista voidaan arvioida muilla ohjelmointia ja muita aihepiirejä käsittelevillä kursseilla, jotka sisältävät sekä jatkuvaa arviointia että loppukokeen. Malli ennustaisi näillä kursseilla lopullisen opiskelijan suoritustason opetusjakson alkuvaiheessa

Computer Science To Go (CS2Go): Developing a course to introduce and teach Computer Science and Computational Thinking to secondary school students

Computer Science To Go (CS2Go) is a course designed to teach Transition Year Students about Computer Science and Computational Thinking. This project has been conducted over two years and this thesis charts the development of the course from the initial research stage, through the lesson creation sections to the testing and evaluation of the course material. Over 80 hours of engaging, informative and challenging material has been developed for use in the classroom. Alongside the lesson plans, assessment and monitoring tools have been created, including a novel tool to assess students Computational Thinking skills. The content was tested in two major studies after an initial pilot study. This initial pilot study proved useful in constructing the full CS2Go course. Overall the course has been well received with teachers and students engaging well with the content. A web portal has also been created to allow for easy dissemination of all the CS2Go material. The further development of this web portal will turn CS2Go into a one-stop shop for teachers and educators hoping to find CS teaching material

The Effects of the COVID-19 Pandemic on the Digital Competence of Educators

The Covid-19 pandemic is having an undeniable impact on all the statements of society. Regarding teaching and learning activities, most educational institutions suspended in-person instruction and moved to remote learning during the lockdown of March and April 2020. Although nowadays many countries have progressively re-opened their educational systems, blended learning is a common practice aimed to reduce the spread of the Covid-19 disease. This disruption has supposed an unprecedented acceleration to the digitalization of teaching and learning. Teaching professionals have been forced to develop their digital competence in a short amount of time, getting mastery in the management of information, the creation of audiovisual contents, and the use of technology to keep their students connected. This Special Issue presents contributions regarding the adoption of distance learning strategies, experiences, or lessons learned in this domain