23,858 research outputs found

    Instructional strategies and tactics for the design of introductory computer programming courses in high school

    Get PDF
    This article offers an examination of instructional strategies and tactics for the design of introductory computer programming courses in high school. We distinguish the Expert, Spiral and Reading approach as groups of instructional strategies that mainly differ in their general design plan to control students' processing load. In order, they emphasize topdown program design, incremental learning, and program modification and amplification. In contrast, tactics are specific design plans that prescribe methods to reach desired learning outcomes under given circumstances. Based on ACT* (Anderson, 1983) and relevant research, we distinguish between declarative and procedural instruction and present six tactics which can be used both to design courses and to evaluate strategies. Three tactics for declarative instruction involve concrete computer models, programming plans and design diagrams; three tactics for procedural instruction involve worked-out examples, practice of basic cognitive skills and task variation. In our evaluation of groups of instructional strategies, the Reading approach has been found to be superior to the Expert and Spiral approaches

    An approach to reconcile the agile and CMMI contexts in product line development

    Get PDF
    Software product line approaches produce reusable platforms and architectures for products set developed by specific companies. These approaches are strategic in nature requiring coordination, discipline, commonality and communication. The Capability Maturity Model (CMM) contains important guidelines for process improvement, and specifies "what" we must have into account to achieve the disciplined processes (among others things). On the other hand, the agile context is playing an increasingly important role in current software engineering practices, specifying "how" the software practices must be addressed to obtain agile processes. In this paper, we carry out a preliminary analysis for reconciling agility and maturity models in software product line domain, taking advantage of both.Postprint (published version

    Web-based learning in the field of empirical research methods

    Get PDF
    This study focuses on the development of a complex web-based learning environment aimed at promoting the acquisition of applicable knowledge in the context of studying empirical research methods at university. This learning environment was then modified further on an empirical basis. The main focus of the present article is to describe the conceptualisation of the learning environment and research activities which were guided by an integrative research paradigm. The learning environment consisted of highly structured, complex texts in which the process of empirical research was illustrated in a detailed manner. By combining these texts with other instructional measures, the learning environment is given a flexible hypertext-structure. The effectiveness of the learning environment as a whole was investigated in three studies (two evaluation studies in the field and one experimental study in the laboratory). It was demonstrated that the additional instructional measures (e.g. a specific feedback-guidance and time-management measures) were not effective. The importance of cognitive, motivational and emotional learning prerequisites for the successful utilisation of the learning environment was highlighted. The implementation of special training and additional preparatory modules is recommended in order to optimise the fit between students' prerequisites and learning environmIm Zentrum der vorliegenden Arbeit steht zum einen die Konzeptualisierung einer Lernumgebung zur Förderung des Erwerbs anwendbaren Wissens im Kontext der universitären Ausbildung in empirischen Forschungsmethoden. Zum anderen werden ausgehend von einem integrativen Forschungsparadigma Forschungsaktivitäten beschrieben, die die empirische Basis zur Weiterentwicklung der Lernumgebung bereitstellen. Die Lernumgebung besteht aus hoch strukturierten, komplexen Texten, in welchen der Prozess empirischer Forschung auf detaillierte Weise veranschaulicht wird. Diese Texte wurden mit anderen instruktionalen Maßnahmen kombiniert, wodurch die Lernumgebung eine flexible, hypertextartige Struktur bekam. Die Effektivität der gesamten Lernumgebung wurde im Rahmen dreier empirischer Studien untersucht, von denen zwei als Evaluationsstudien im Feld durchgeführt wurden; die dritte war eine experimentelle Laborstudie. Es wurde gezeigt, dass die zusätzlichen instruktionalen Maßnahmen (z. B. eine spezifische Feedback-Anleitung und eine Zeitmanagement-Maßnahme) nicht wirksam waren. Die Bedeutung kognitiver, motivationaler und emotionaler Lernvoraussetzungen für die erfolgreiche Nutzung der Lernumgebung konnte nachgewiesen werden. Um die Passung zwischen den Eingangsvoraussetzungen der Studierenden und der Lernumgebung zu verbessern, wurde die Implementation eines speziellen Trainings und eines zusätzlichen vorbereitenden Moduls vorgeschlag

    Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

    Full text link
    In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero shot" feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world's largest programming education platform.Comment: To appear at AAAI 2019; 9 page

    QuizPower: a mobile app with app inventor and XAMPP service integration

    Get PDF
    This paper details the development of a mobile app for the Android operating system using MIT App Inventor language and development platform. The app, Quiz Power, provides students a way to study course material in an engaging and effective manner. At its current stage the app is intended strictly for use in a mobile app with App Inventor course, although it provides the facility to be adapted for other courses by simply changing the web data store. Development occurred during the spring semester of 2013. Students in the course played a vital role in providing feedback on course material, which would be the basis for the structure of the quiz as well as the questions. The significance of the project is the integration of the MIT App Inventor service with a web service implemented and managed by the department

    An Exploration of Student Reasoning about Undergraduate Computer Science Concepts: An Active Learning Technique to Address Misconceptions

    Get PDF
    Computer science (CS) is a popular but often challenging major for undergraduates. As the importance of computing in the US and world economies continues to grow, the demand for successful CS majors grows accordingly. However, retention rates are low, particularly for under-represented groups such as women and racial minorities. Computing education researchers have begun to investigate causes and explore interventions to improve the success of CS students, from K-12 through higher education. In the undergraduate CS context, for example; student difficulties with pointers, functions, loops, and control flow have been observed. We and others have utilized student responses to multiple choice questions aimed at determining misconceptions, engaged in retroactive examination of code samples and design artifacts, and conducted interviews in an attempt to understand the nature of these problems. Interventions to address these problems often apply evidenced-based active learning techniques in CS classrooms as a way to engage students and improve learning.In this work, I employ a human-centered approach, one in which the focus of data collection is on the student thought processes as evidenced in their speech and writing. I seek to determine what students are thinking not only through what can be surmised in retrospect from the artifacts they create, but also to gain insight into their thoughts as they engage in the design, implementation,and analysis of those artifacts and as they reflect on those processes and artifacts shortly after. For my dissertation work, I have conducted four studies: 1. a conceptual assessment survey asking students to “Please explain your reasoning” after each answer to code tracing/execution questions followed by task-based interviews with a smaller, different group of students 2. a “coding in the wild” think aloud study that recorded the screen and audio of students as they implemented a simple program and explained their thought process 3. interview analyses of student design diagrams/documentation in a software engineering course, tasking students to explain their designs and comparing what they believed they had designed with what is actually shown from their submitted documentation. These first three studies were formative, leading to some key insights including the benefits students can gain from feedback, students’ tendencies to avoid complexity when programming or encountering concepts they do not fully grasp, the nature of student struggles with the planning stages of problem solving, and insight into the fragile understanding of some key CS concepts that students form. I leverage the benefits of feedback with guided prompts using the misconceptions uncovered in my formative studies to conduct a final, evaluative study. This study seeks to evaluate the benefits that can be gained from a guided feedback intervention for learning introductory programming concepts and compare those benefits and the effort and resource costs associated with each variation, comparing the costs and benefits associated with two forms of feedback. The first is an active learning technique I developed and deem misconception-based feedback (MBF), which has peers working in pairs use prompts based on misconceptions to guide their discussion of a recently completed coding assignment. The second is a human autograder (HAG) group acting as a control. HAG simulates typical autograders, supplying test cases and correct solutions, but utilizes a human stand-in for a computer. In both conditions, one student uses provided prompts to guide the discussion. The other student responds/interacts with their code based on the prompts. I captured screen and audio recordings of these discussions. Participants completed conceptual pre-tests and post-tests that asked them to explain their reasoning. I hypothesized that the MBF intervention will offer avaluable way to increase learning, address misconceptions, and get students more engaged that will be feasible in CS courses of any size and have benefits over the HAG intervention. Results show that for questions involving parameter passing with regards to pass by reference versus pass by value semantics, particularly with pointers, there were significant improvements in learning outcomes for the MBF group but not the HAG group
    • …
    corecore