Automaattinen arviointi opetuskäytössä ohjelmoinnin peruskursseilla: Yhteenveto lähestymistavoista ja tapaustutkimus

Ohjelmointi on muuttumassa yhdeksi digitalisoituvan yhteiskunnan kysytyimmistä taidoista. Samaan aikaan ohjelmoinnin osaajia on liian vähän. Kysynnän kasvaessa ohjelmoinnin opetukselle, kohdistuu opetuksen tarjoajille painetta kasvattaa kurssikokoja, mikä tuottaa lisää arviointityötä. Kurssihenkilökunnalle varatut resurssit ovat kuitenkin rajallisia, joten työtaakkaa helpottamaan on kehitetty automaattisia arviointijärjestelmiä. Tässä työssä luodaan katsaus olemassa oleviin automaattisen arvioinnin menetelmiin, mitä arviointijärjestelmien kehittäjät voivat hyödyntää työssään. Työssä tunnistetaan ohjelmoinnin alkeiskursseille soveltuvia tehtävätyyppejä, kategorisoidaan palautteen laatua ja näkökulmia testaukseen, sekä vedetään yhteen testausstrategioita. Löydösten perusteella arvioidaan Tampereen yliopistolla ohjelmoinnin alkeiskurssilla käytössä olevaa arviointijärjestelmää. Järjestelmän todetaan olevan ohjelmoinnin opetukseen soveltuva ympäristö. Järjestelmän käytöllä arviointityötä saadaan automatisoitua, joka mahdollistaa suuren tehtävien ja palautteen määrän, palautteen välittömyyden ja kurssin skaalautuvuuden suurillekin opiskelijamassoille. Järjestelmästä tunnistetaan kirjallisuusselvityksen perusteella kuitenkin myös kehityskohteita, kuten tyylintarkastamisen puuttuminen

Mining Student Submission Information to Refine Plagiarism Detection

Plagiarism is becoming an increasingly important issue in introductory programming courses. There are several tools to assist with plagiarism detection, but they are not effective for more basic programming assignments, like those in introductory courses. The proliferation of auto-grading platforms creates an opportunity to capture additional information about how students develop the solutions to their programming assignments. In this research, we identify how to extract information from an online autograding platform, Mimir Classroom, that can be useful in revealing patterns in solution development. We explore how and to what extent this additional information can be used to better support instructors when identifying cases of probable plagiarism. We have developed a tool that takes the raw student assignment submissions from Mimir, analyzes them, and produces data sets and visualizations that help instructors to refine information extracted by existing plagiarism detection platforms. The instructors can then take this information to further investigate any probable cases of plagiarism that have been found by the tool. Our main goal is to give insight into student behaviors and identify signals that can be effective indicatives of plagiarism. Furthermore, the framework can enable the analysis of other aspects of students’ solution development processes that may be useful when reasoning about their learning. As an initial exploration scenario of the framework developed in this work, we have used student code submissions from the CSCE 121: Introduction to Program Design and Concepts course at Texas A&M University. We experimented with the student code submissions from the Fall 2018 and Fall 2019 offerings of the course

Desenvolvimento de um analisador e avaliador de código de App Inventor para ensino de computação

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação.Com a evolução da tecnologia e a sua inserção em todas as áreas profissionais e pessoais dos cidadãos, percebe-se a necessidade de incluir o ensino do pensamento computacional no Ensino Básico. O pensamento computacional envolve o raciocínio lógico e matemático, desenvolvimento e análise de algoritmos, representação de dados e técnicas de programação para a solução de problemas. Tipicamente o pensamento computacional é ensinado por meio de unidades instrucionais que envolvem trabalhos práticos de programação utilizando ambientes de programação para iniciantes. Um exemplo é o App Inventor, um ambiente de programação baseado em blocos que possibilita o desenvolvimento de apps para dispositivos móveis Android. Neste contexto educacional, para facilitar a avaliação dos trabalhos práticos de programação é importante que o professor tenha auxilio por meio de ferramentas. Deste modo, o objetivo deste trabalho é desenvolver uma ferramenta que analisa e avalia automaticamente o código desenvolvido pelos alunos usando o App Inventor, auxiliando os professores na avaliação dos trabalhos práticos realizados. O analisador e avaliador de código é desenvolvido com base na literatura seguindo uma abordagem sistemática de desenvolvimento de software. Por meio da disponibilidade do analisador e avaliador de código, espera-se facilitar e reduzir o esforço necessário para avaliação de trabalhos de alunos no ensino de computação no Ensino Básico

Κατηγοριοποίηση και περιληπτικές αποδόσεις εργασιών συνεδρίων της ACM SIGCSE

Η παρούσα εργασία αφορά στη μελέτη εργασιών οι οποίες παρουσιάστηκαν στο συνέδριο ACM SIGCSE τις χρονιές 2016, 2017 και 2018. Αρχικά, γίνεται μια κατηγοριοποίηση, με βάση τον κύριο τομέα της Εκπαίδευσης της Πληροφορικής τον οποίο αφορά η κάθε εργασία που παρουσιάστηκε στα προαναφερθέντα συνέδρια. Οι κατηγορίες στις οποίες κατατάχθηκαν τα άρθρα είναι οι εξής: • Αξιολόγηση σπουδαστών • Ασφάλεια και προστασία της ιδιωτικής ζωής • Διαδραστικά περιβάλλοντα μάθησης • Διαφορετικότητα των φύλων/ Πολυπολιτισμικότητα • Εκπαίδευση της Μηχανικής Λογισμικού • Εισαγωγή στην Πληροφορική • Εκπαίδευση της Πληροφορικής • Ενσωμάτωση Πληροφορίας • Ηλεκτρονική μάθηση • Οπτικοποίηση • Πρότυπα αναλυτικά προγράμματα • Πρωτοβάθμια και Δευτεροβάθμια Εκπαίδευση • Συνεργατική Μάθηση • Συστήματα διαχείρισης μάθησης • Υπολογιστική Σκέψη • Υπολογιστικός Αλφαβητισμός Στη συνέχεια, δίνονται περιληπτικές αποδόσεις των εργασιών της χρονιάς 2017 που εμπίπτουν στις παρακάτω επιλεγμένες κατηγορίες: • Αξιολόγηση φοιτητών/μαθητών • Εισαγωγή στην Πληροφορική • Εκπαίδευση της Πληροφορικής • Πρωτοβάθμια και Δευτεροβάθμια Εκπαίδευση • Συνεργατική Μάθηση • Υπολογιστική ΣκέψηThis thesis focuses on the study of papers presented at the ACM SIGCSE conference in the years 2016, 2017 and 2018. Initially, a categorization is defined, based on the main areas of IT education that are included in the aforementioned conferences. The categories in which the articles were classified are: • Student evaluation • Security and Privacy • Interactive learning environments • Gender Diversity / Multiculturalism • Software engineering education • CS1 • Computer Science Education • Integration of Information • E-learning • Visualization • Model curricula • K-12 • Collaborative learning • Computational Thinking • Computing Literacy Afterwards, reviews of the papers of the year 2017 are presented concerning the following categories: • Student evaluation • CS1 • Computer Science Education • K-12 • Collaborative learning • Computational Thinkin

Desenvolvimento de um Analisador de Design de Interface no Contexto do Ensino de Computação com o App Inventor

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Ciências da Computação.O uso cotidiano de tecnologias de informação e comunicação pela população tem se tornado um componente imprescindível, tanto na vida pessoal como na carreira profissional. Por isso, a inclusão do ensino dos princípios básicos da Ciência da Computação na Educação Básica acaba sendo conhecimento útil para o cidadão como um usuário de tecnologias de informação, mas também o torna capacitado para ser criador. De forma a incluir o ensino da computação na Educação Básica, há iniciativas que propõem a utilização de ambientes de programação visual baseado em blocos, como Scratch, Snap! e App Inventor. Para auxiliar estudantes a aprender competências básicas de computação incluindo o pensamento computacional por meio da programação, aprimorar a capacidade de raciocínio lógico para resolução de problemas, inovação e criatividade. Uma forma de ensinar computação é por meio de criação de aplicativos para dispositivos Android, com auxílio do App Inventor. Além da programação é essencial o design de interface, pois procura trazer aos usuários a melhor usabilidade. Isso torna importante ensinar não só a programação, mas também conceitos básicos de design de interface. Para proporcionar um processo de aprendizagem consistente é importante apresentar um feedback em forma de avaliação. Isso é uma tarefa complicada e trabalhosa para os professores. Visando facilitar a avaliação, existem ferramentas que analisam automaticamente os projetos de programação dos alunos e apresentam um feedback sobre a variedade e complexidade da programação realizada, como por exemplo Dr. Scratch e CodeMaster. Considerando projetos desenvolvidos pelo App Inventor, ainda não há ferramentas que avaliam o design de interfaces de apps desenvolvidos de forma automatizada. Assim, o objetivo deste trabalho é a aprimoração da ferramenta web CodeMaster, de forma a incluir a análise e avaliação automatizada de design de interface dos aplicativos desenvolvidos com App Inventor. Desta forma, o presente trabalho visa levantar o estado da arte, mapear as heurísticas de usabilidade a partir de um guia de design, elaborar uma rubrica, desenvolver o sistema e, por fim, realizar testes de corretude e validade. Assim, como resultado é apresentada uma rubrica que avalia o design de interface de aplicativos desenvolvidos no App Inventor. Também é apresentada a ferramenta CodeMaster, com a avaliação automatizada da rubrica. Com o objetivo de auxiliar professores da Educação Básica na avaliação de projetos e deste modo facilitar o ensino e popularização de computação e design de interfaces no Brasil

Social Addictive Gameful Engineering (SAGE): A Game-based Learning and Assessment System for Computational Thinking

At an unrivaled and enduring pace, computing has transformed the world, resulting in demand for a universal fourth foundation beyond reading, writing, and arithmetic: computational thinking (CT). Despite increasingly widespread acceptance of CT as a crucial competency for all, transforming education systems accordingly has proven complex. The principal hypothesis of this thesis is that we can improve the efficiency and efficacy of teaching and learning CT by building gameful learning and assessment systems on top of block-based programming environments. Additionally, we believe this can be accomplished at scale and cost conducive to accelerating CT dissemination for all. After introducing the requirements, approach, and architecture, we present a solution named Gameful Direct Instruction. This involves embedding Parsons Programming Puzzles (PPPs) in Scratch, which is a block-based programming environment currently used prevalently in grades 6-8. PPPs encourage students to practice CT by assembling into correct order sets of mixed-up blocks that comprise samples of well-written code which focus on individual concepts. The structure provided by PPPs enable instructors to design games that steer learner attention toward targeted learning goals through puzzle-solving play. Learners receive continuous automated feedback as they attempt to arrange programming constructs in correct order, leading to more efficient comprehension of core CT concepts than they might otherwise attain through less structured Scratch assignments. We measure this efficiency first via a pilot study conducted after the initial integration of PPPs with Scratch, and second after the addition of scaffolding enhancements in a study involving a larger adult general population. We complement Gameful Direct Instruction with a solution named Gameful Constructionism. This involves integrating with Scratch implicit assessment functionality that facilitates constructionist video game (CVG) design and play. CVGs enable learner to explore CT using construction tools sufficiently expressive for personally meaningful gameplay. Instructors are enabled to guide learning by defining game objectives useful for implicit assessment, while affording learners the opportunity to take ownership of the experience and progress through the sequence of interest and motivation toward sustained engagement. When strategically arranged within a learning progression after PPP gameplay produces evidence of efficient comprehension, CVGs amplify the impact of direct instruction by providing the sculpted context in which learners can apply CT concepts more freely, thereby broadening and deepening understanding, and improving learning efficacy. We measure this efficacy in a study of the general adult population. Since these approaches leverage low fidelity yet motivating gameful techniques, they facilitate the development of learning content at scale and cost supportive of widespread CT uptake. We conclude this thesis with a glance at future work that anticipates further progress in scalability via a solution named Gameful Intelligent Tutoring. This involves augmenting Scratch with Intelligent Tutoring System (ITS) functionality that offers across-activity next-game recommendations, and within-activity just-in-time and on-demand hints. Since these data-driven methods operate without requiring knowledge engineering for each game designed, the instructor can evolve her role from one focused on knowledge transfer to one centered on supporting learning through the design of educational experiences, and we can accelerate the dissemination of CT at scale and reasonable cost while also advancing toward continuously differentiated instruction for each learner