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.

This dissertation study explored: 1) instructors’ use of discussion strategies that enhance meaningful learner interactions in online discussions and student performance, and 2) learners’ interaction patterns in online discussions that lead to better student performance in online introductory mathematics courses. In particular, the study applied a set of data mining techniques to a large-scale dataset automatically collected by the Canvas Learning Management System (LMS) for five consecutive years at a public university in the U.S., which included 2,869 students enrolled in 72 courses. First, the study found that the courses that posted more open-ended prompts, evaluated students’ discussion messages posted by students, used focused discussion settings (i.e., allowing a single response and replies to that response), and provided more elaborated feedback had higher students final grades than those which did not. Second, the results showed the instructors’ use of discussion strategies (discussion structures) influenced the quantity (volume of discussion), the breadth (distribution of participation throughout the discussion), and the quality of learner interactions (levels of knowledge construction) in online discussions. Lastly, the results also revealed that the students’ messages related to allocentric elaboration (i.e., taking other peers’ contributions in argumentive or evaluative ways) and application (i.e., application of new knowledge) showed the highest predictive value for their course performance. The findings from this study suggest that it is important to provide opportunities for learners to freely discuss course content, rather than creating a discussion task related to producing a correct answer, in introductory mathematics courses. Other findings reported in the study can also serve as guidance for instructors or instructional designers on how to design better online mathematics courses.

A learning analytics dashboard (LAD) is an application that illustrates the activity and progress of a user in a self-regulated, online learning environment. This tool mines source data to provide meaningful information that supports decision making and positively impacts learning behaviour. Research on this topic explores how learning activities and pedagogical goals are impacted by integrating LADs into learning and/or teaching environments. Currently, the majority of the research is centred around predicting student academic performance and identifying students that are at risk of failing. The popularity of integrating technology into educational practices has led to the adoption of LADs into learning management systems (LMS) or massive open online courses (MOOCs). The objective of this paper is to develop a concept for a standalone prototype LAD, for an Introductory Statistics course (STA 1000), to be potentially integrated into the University of Cape Town's (UCT) LMS, Vula. The dashboard aims to create and incorporate meaningful visualisations, that have the potential to primarily assist students as well as educators. Visualised information in the LAD aims to positively impact students to enhance and drive effective learning, which could consequentially aid educators. Additionally, the dashboard will aim to provide actionable feedback, derived from predictive modelling and course analytics, that positively impacts learning behaviour and identifies factors that the student could most effectively use to leverage their chances of passing and improve academic performance. Predictive analytics aim to identify academic factors, that a student has control over, such as course assessments and engagement variables, at certain time points in the academic semester and provide a useful course of action at those time points. Other than variables measured throughout the course, the predictive modelling takes certain prior academic information into consideration.

Rapid developments in the educational system significantly facilitate the popularisation of education, making education more accessible. The learning process should be systematically analyzed to assist teaching, however, traditional learning systems provide limited support for educators. The learning analytics dashboard is, therefore, becoming a popular tool to provide insights into the learning process by analyzing and interpreting the collected data with effective visualizations. With the purpose of improving pedagogical decision-making, it is of importance to provide a comprehensive overview and informative feedback for the learning process. This study aims to explore the affordances and challenges of designing a comprehensive learning analytics dashboard for educators to support personalized learning and adaptive teaching. A large amount of research on the learning analytics dashboard was reviewed as the theoretical foundation. The prototype of the dashboard was developed based on the obtained theories. To examine the prototype, a user study with user experience designers was conducted to evaluate the clarity of the visualization and the effectiveness of the dashboard. The results summarised from the thematic analysis indicated that the dashboard could serve as a potentially valuable tool with overall clear interaction to support educators, and the visualization was considered intuitive to understand the connection of multiple data points. Suggestions for designing a learning analytics dashboard are discussed in terms of visualization, consistency, conception, navigation, interaction, information via text, visual appearance, functionality, and privacy. To further improve the affordances of the dashboard, future research with educators is of importance to conduct. / Den snabba tekniska utvecklingen inom utbildningsområdet har avsevärt underlättat populariseringen av utbildningen och gjort den mer tillgänglig. Inlärningsprocessen bör analyseras systematiskt för att stödja denna utveckling, men traditionella inlärningssystem ger begränsat stöd för lärare. Instrumentpaneler (dashboards) för inlärningsanalys har därför blivit ett populärt verktyg för att ge inblick i inlärningsprocessen genom att analysera och tolka den insamlade informationen med effektiva visualiseringar. I syfte att förbättra det pedagogiska beslutsfattandet är det viktigt att ge en omfattande översikt och informativ och kontinuerlig feedback för inlärningsprocessen. Denna studie syftar till att utforska fördelar och utmaningar med att utforma en omfattande instrumentpanel för lärandeanalys för lärare att stödja den individuella inlärningen och därmed kunna anpassa undervisningen. En stor mängd forskning på instrumentpaneler för inlärningsanalys granskades som underlag. Prototypen på instrumentpanelen utvecklades baserat på detta underlag. För att utvärdera prototypen genomfördes en användarstudie med UX-designers för att avgöra visualiseringens tydlighet och instrumentpanelens effektivitet. Resultaten från den tematiska analysen indikerade att instrumentpanelen kunde fungera som ett potentiellt värdefullt verktyg med en överlage tydlig interaktion för att stödja lärare och visualiseringen ansågs vara intuitiv för att förstå relationen mellan datapunkterna. Förslag för att utforma en instrumentpanel för inlärningsanalys diskuteras avseende visualisering, konsistens, uppfattning, navigering, interaktion, information via text, visuellt utseende, funktionalitet och integritet. För att ytterligare förbättra instrumentpanelens är framtida utveckling och utvärdering med pedagoger viktig att genomföra.

Learning Analytic Dashboards have proven to help students reach their academic goals by tracking their study data to offer feedback. However, current dashboard research focuses on traditional education, such as schools and universities, rather than emerging educational sectors like coding boot camps that have different curricula and structures. Based on literature research and user experience methods, an interactive prototype was designed to help boot camp students track "hard skills" and "soft skills." Design experts and boot camp students evaluated this prototype to provide feedback about its usability and effectiveness. The study found that the students appreciated the focus on learning goals instead of tracking metrics like home completion rate or exam grades. At the same time, the polished user interface kept the students engaged. The contribution to the Learning Analytics field is the demographic data of boot camp students in Stockholm through user personas, recommendations to avoid common dashboard limitations, and suggesting ideas for further evaluation / (Note: This is a Google Translation, i will write the final one, after the abstract in english is approved) Coding boot camps är snabba program som hjälper människor att få jobb inom IT genom att lära dem hur man kodar. Ändå är det inte alla studenter som får jobb efter examen på grund av de olika kunskapsnivåerna. Learning Analytic Dashboards har visat sig hjälpa elever att nå sina akademiska mål genom att spåra deras studiedata för att ge feedback. Men aktuell instrumentpanelforskning fokuserar på traditionell utbildning, såsom skolor och universitet, som har olika läroplaner och strukturer. Därför undersöker denna avhandling frågan om "Hur man designar en instrumentpanel för det speciella fallet med boot camp-studenter?" Baserat på litteraturforskning och metoder för användarupplevelser designades en interaktiv prototyp för att hjälpa eleverna att spåra "hårda färdigheter" och "mjuka färdigheter". Designexperter och boot camp-studenter utvärderade denna prototyp för att ge feedback om dess användbarhet och effektivitet. Den här studien drar slutsatsen att instrumentpaneler för startläger bör prioritera mätvärden för lärandemål i stället för att spåra mätvärden som slutförandegrad av hem eller provbetyg, integrera studentfeedback i de tidigare stadierna av designen och leverera ett polerat användargränssnitt för att hålla eleverna engagerade. Avhandlingen uppmuntrar framtida forskning genom att tillhandahålla information om studentdemografi i Stockholm, Sverige, genom användarpersonas, designrekommendationer och föreslå idéer för vidare utvärdering.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24732号 / 情博第820号 / 新制||情||138(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 伊藤 孝行, 准教授 近藤 一晃 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24733号 / 情博第821号 / 新制||情||138(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 伊藤 孝行, 准教授 馬 強 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24734号 / 情博第822号 / 新制||情||138(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 伊藤 孝行, 准教授 馬 強 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The data provided by educational platforms and digital tools offers new ways of analysing students’ learning strategies. One such digital tool is the wellbeing platform created by EdAider, which consists of an interface where students can answer questions about their well-being, and a dashboard where teachers and schools can see insights into the well-being of individual students and groups of students. Both students and teachers can see the development of student well-being on a weekly basis. This thesis project investigates how Machine Learning (ML) can be used along side Learning Analytics (LA) to understand and improve students’ well-being. Real-world data generated by students at Swedish schools using EdAider’s well-being platform is analysed to generate data insights. In addition ML methods are implemented in order to build a model to predict whether students are at risk of failing based from their well-being data, with the goal to inform data-driven improvements of students’ education. This thesis has three primary goals which are to: 1. Generate data insights to further understand patterns in the student wellbeing data. 2. Design a classification model using ML methods to predict student performance based on well-being data, and validate the model against actual performance data provided by the schools. 3. Carry out an ethical evaluation of the data analysis and grade prediction model. The results showed that males report higher well-being on average than females across most well-being factors, with the exception of relationships where females report higher well-being than males. Students identifying as non-binary gender report a considerably lower level of well-being compared with males and females across all 8 well-being factors. However, the amount of data for non-binary students was limited. Primary schools report higher well-being than the older secondary school students. Students reported anxiety/depression as the most closely correlated dimensions, followed by engagement/accomplishment and positive emotion/depression. Logistic regression and random forest models were used to build a performance prediction model, which aims to predict whether a student is at risk of performing poorly based on their reported well-being data. The model achieved accuracy of 80-85 percent. Various methods of feature importance including regularization, recursive feature selection, and impurity decrease for random forest were investigated to examine which well-being factors have the most effect on performance. All methods of examining feature importance consistently identified three features as important: ”accomplishment,” ”depression,” and ”number of surveys answered.” The benefits, risks and ethical value conflicts of the data analysis and prediction model were carefully considered and discussed using a Value Sensitive Design approach. Ethical practices for mitigating risks are discussed. / Den data som tillhandahålls av utbildningsplattformar och digitala verktyg erbjuder nya sätt att analysera studenters inlärningsstrategier. Ett sådant digitalt verktyg är mående plattformen skapad av EdAider, som består av ett gränssnitt där elever kan svara på frågor om deras mående, och en dashboard där lärare och skolor kan se insikter om individuella elevers och grupper av elevers mående. Både elever och lärare kan se utvecklingen av elevers mående på veckobasis. Detta examensarbete undersöker hur Maskininlärning (ML) kan användas tillsammans med Inlärningsanalys (LA) för att förstå och förbättra elevers mående. Verkliga data genererade av elever vid svenska skolor med hjälp av EdAiders måendeplattform analyseras för att skapa insikter om data. Dessutom implementeras ML-metoder för att bygga en modell för att förutsäga om elever riskerar att misslyckas baserat på deras mående-data, med målet att informera data-drivna förbättringar av elevers utbildning. Detta examensarbete har tre primära mål: 1. Skapa datainsikter för att ytterligare förstå mönster i data om elevers mående. 2. Utforma en modell med hjälp av ML-metoder för att förutsäga elevprestationer baserat på mående-data, och validera modellen mot faktiska prestationsdata som tillhandahålls av skolorna. 3. Utföra en etisk utvärdering av dataanalysen och modellen för betygsprediktion. Resultaten visade att pojkar i genomsnitt rapporterar högre mående än flickor inom de flesta måendefaktorer, med undantag för relationer där flickor rapporterar högre mående än pojkar. Elever som identifierar sig som icke-binära rapporterar en betydligt lägre nivå av mående jämfört med pojkar och flickor över alla 8 måendefaktorer. Men mängden data för icke-binära elever var begränsad. Grundskolor rapporterar högre mående än äldre gymnasieelever. Elever rapporterade ångest/depression som de mest nära korrelerade dimensionerna, följt av engagemang/prestation och positivt känsloläge/depression. Logistisk regression och random forest-modeller användes för att bygga en prestationsprediktionmodell, med en noggrannhet på 80-85 procent uppnådd. Olika metoder för feature selection undersöktes, inklusive regularisering, recursive feature selection och impurity decrease för random forest. Alla metoder för undersökning av feature selection identifierade konsekvent tre funktioner som viktiga: ”prestation,” ”depression,” och ”antal svarade enkäter.” Fördelarna, riskerna och etiska värdekonflikterna i dataanalysen och prediktionsmodellen beaktades noggrant och diskuterades med hjälp av en Value Sensitive Design-ansats.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24260号 / 情博第804号 / 新制||情||136(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 伊藤 孝行, 教授 吉川 正俊 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM