Approaches for Coordinating eTextbooks, Online Programming Practice, Automated Grading, and More into One Course

Ellis, Margaret; Shaffer, Clifford A.; Edwards, Stephen H.

doi:10.1145/3287324.3287487

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…In the educational context, autograders play a crucial role in grading assignments, relieving instructors of some of their workload and conserving resources (Ellis et al, 2019;Galvão et al, 2016;Hollingsworth, 1960). However, the use of autograders can also influence the development of undesirable habits in students.…”

Section: Introductionmentioning

confidence: 99%

When Test Cases Are Not Enough: Identification, Assessment, and Rationale of Misconceptions in Correct Code (MC³)

Silva,

Caceffo,

Azevedo

2023

RBIE

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Automated grading systems (autograders) assist the process of teaching in introductory programming courses (CS1). However, the sole focus on correctness can obfuscate the assessment of other characteristics present in code. In this work, we investigated if code, deemed correct by an autograder, were developed with characteristics that indicated potential misunderstandings of the concepts taught in CS1. These characteristics were nominated Misconceptions in Correct Code (MC³). By analyzing 2,441 codes developed by CS1 students, we curated an initial list of 45 MC³. This list was assessed by CS1 instructors, resulting in the identification of the misconceptions that most needed addressing in classes. We selected the 15 most severe MC³ for further investigation, including a semi-structured observation in a CS1 course and a prototype of an automated detection using static analysis of code. The obtained results suggested that students develop these misconceptions either due to an incomplete comprehension of the concepts taught in the CS1 course or a lack of attention while elaborating their code, with correctness being their primary goal. We believe our results can contribute to: (1) the research field of misconceptions in CS1; (2) promoting alternative approaches to complement the use of autograders in CS1 classes; and (3) providing insights that can serve as the foundation for teaching interventions involving MC³ in CS1.

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Section: Introductionmentioning

confidence: 99%

When Test Cases Are Not Enough: Identification, Assessment, and Rationale of Misconceptions in Correct Code (MC³)

Silva,

Caceffo,

Azevedo

2023

RBIE

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“…One reason for that is often the way programming is taught. Given the fact that learning programming requires a certain degree of procedural knowledge competence, learning programming needs practice and experience [11,34,84]. Unfortunately, teaching and learning content in programming education is often heavy on declarative knowledge, focusing on the features and particularities of various programming languages [49], especially in cases when a new programming language is taught to students with previous programming knowledge [85].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Assessment and Content Recommendation in Online Programming Courses: On the Use of Elo-rating

Vesin

Mangaroska

Akhüseyinoğlu

et al. 2022

ACM Trans. Comput. Educ.

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Online learning systems should support students preparedness for professional practice, by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students’ development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts, but to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both, students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students’ knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students’ proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

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“…Autograders são sistemas de avaliação automática de programas normalmente empregados para auxiliar a correção de exercícios [14], reduzindo o tempo que o docente normalmente levaria para fazê-lo [7,8]. No entanto, seu uso também está sujeito a falhas: um mal funcionamento dessa ferramenta pode gerar desconforto nos estudantes, que estão dependentes da mesma para enviar trabalhos acadêmicos [11].…”

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Análise Estática de Código em Conjunto com Autograders

Silva¹,

Caceffo²,

Azevedo³

2021

Anais Estendidos Do I Simpósio Brasileiro De Educação Em Computação (EDUCOMP Estendido 2021)

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Este trabalho tem como objetivo o estudo e desenvolvimento de técnicas que visam auxiliar alunos e professores de disciplinas de introdução à programação nas universidades (CS1). Estas disciplinas, em que estudos reportam altos índices de evasão, são cada vez mais relevantes no ambiente acadêmico, sendo oferecidas não apenas a alunos de computação, mas também no escopo de cursos de STEM (ciência, tecnologia, engenharia e matemática). Neste momento, deseja-se utilizar sistemas de correção automática de código (autograders) como forma de obter e mostrar feedback além do resultado se um determinado programa passa ou não nos conjuntos de casos de teste. Para isto, uma análise estática de código está sendo realizada com soluções de exercícios submetidos por alunos de uma disciplina de introdução à programação da UNICAMP buscando identificar problemas de compreensão simples por meio de métricas. Numa análise geral, existem submissões de alunos com até sete vezes mais estruturas condicionais que a solução oficial e a média da turma para o mesmo exercício. Esse tipo de feedback pode alertar o docente e o aluno de que este padrão de programação pode levar a problemas futuros. Além disso, a proposta desta pesquisa também objetiva aplicar esses feedbacks em conjunto com a Aprendizagem Ativa, de forma a estimular discussões, trazendo os alunos para um maior engajamento em sala de aula. Por fim, com a inclusão destas intervenções também deseja-se avaliar o impacto na carga de trabalho dos docentes, com a expectativa de que seja reduzida. Link para o vídeo da apresentação: https://youtu.be/ghBTMRN2jEQ

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Approaches for Coordinating eTextbooks, Online Programming Practice, Automated Grading, and More into One Course

Cited by 7 publications

References 24 publications

When Test Cases Are Not Enough: Identification, Assessment, and Rationale of Misconceptions in Correct Code (MC³)

When Test Cases Are Not Enough: Identification, Assessment, and Rationale of Misconceptions in Correct Code (MC³)

Adaptive Assessment and Content Recommendation in Online Programming Courses: On the Use of Elo-rating

Análise Estática de Código em Conjunto com Autograders

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