Using Video Game Development to Motivate Program Design and Algebra Among Inner-City High School Students

• URL: http://arxiv.org/abs/2008.12115v1
• Date: Fri, 21 Aug 2020 01:22:35 GMT
• Title: Using Video Game Development to Motivate Program Design and Algebra Among Inner-City High School Students
• Authors: Marco T. Moraz\'an (Seton Hall University)
• Abstract summary: This article presents a novel approach to teaching program design to inner-city high school students. The approach is based on a design recipe to help students develop the abstractions that lead to functions. Students are taught how to use high school algebra concepts, like compound functions and function composition, to also design functions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Introducing inner-city high school students to program design presents unique challenges. The typical assumptions of an introductory programming course, like students understand what variables and functions are, may not be safe. Therefore, asking students to define functions as part of the program design process may be an overwhelming task. Many students do not understand that a function is an abstraction over similar expressions and that parameters represent the differences among these expressions. This articles presents a novel approach to teaching program design to high school students while simultaneously reinforcing high school algebra. The approach is based on a design recipe to help students develop the abstractions that lead to functions. Using a bottom-up approach, students are taught how to abstract over similar expressions. They are then taught how to use high school algebra concepts, like compound functions and function composition, to also design functions. In addition, the article also presents empirical data collected from students to measure their reaction to the course. For the students in the course, the empirical data suggests that high school algebra concepts are successfully reinforced and that students feel they become better problem solvers, find programming intellectually stimulating, and walk away with an interest in programming.

Related papers

• Modeling Student Learning with 3.8 Million Program Traces [52.153493498021895]
  We introduce a dataset of over 3.8 million programming reasoning traces from users of Pencil Code.<n>We find that models trained on real traces are stronger at modeling diverse student behavior.<n>We show that we can help students recover from mistakes by steering code generation models to identify a sequence of edits that will result in more correct code.
  arXiv  Detail & Related papers  (2025-10-06T17:37:17Z)
• Improving Question Embeddings with Cognitiv Representation Optimization for Knowledge Tracing [77.14348157016518]
  The Knowledge Tracing (KT) aims to track changes in students' knowledge status and predict their future answers based on their historical answer records. Current research on KT modeling focuses on predicting student' future performance based on existing, unupdated records of student learning interactions. We propose a Cognitive Representation Optimization for Knowledge Tracing model, which utilizes a dynamic programming algorithm to optimize structure of cognitive representations.
  arXiv  Detail & Related papers  (2025-04-05T09:32:03Z)
• Representational Alignment Supports Effective Machine Teaching [81.19197059407121]
  GRADE is a new controlled experimental setting to study pedagogy and representational alignment. We find that improved representational alignment with a student improves student learning outcomes. However, this effect is moderated by the size and representational diversity of the class being taught.
  arXiv  Detail & Related papers  (2024-06-06T17:48:24Z)
• Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training [65.10741459705739]
  We propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo. We first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes. Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy.
  arXiv  Detail & Related papers  (2023-01-18T14:23:29Z)
• Identifying Different Student Clusters in Functional Programming Assignments: From Quick Learners to Struggling Students [2.0386745041807033]
  We analyze student assignment submission data collected from a functional programming course taught at McGill university. This allows us to identify four clusters of students: "Quick-learning", "Hardworking", "Satisficing", and "Struggling" We then analyze how work habits, working duration, the range of errors, and the ability to fix errors impact different clusters of students.
  arXiv  Detail & Related papers  (2023-01-06T17:15:58Z)
• Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
  Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. Standard approaches require instructors to manually grade student-implemented interactive programs. Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
  arXiv  Detail & Related papers  (2022-11-16T10:00:23Z)
• Introduction to Functional Classes in CS1 [0.0]
  Students rarely connect first-class functions to objects and object-oriented program design. This article describes how students are introduced to objects within the setting of a design-based introduction to programming that uses a functional language.
  arXiv  Detail & Related papers  (2022-07-26T07:45:20Z)
• Design of Classes I [0.0]
  This article presents how the first steps of this transition have been successfully implemented at Seton Hall University. The transition is made smooth by explicitly showing students that the design lessons they have internalized are relevant in object-oriented programming. Empirical evidence collected from students in the course suggests that the approach developed is effective and that the transition is smooth.
  arXiv  Detail & Related papers  (2022-07-26T07:44:24Z)
• From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks [20.64766977405438]
  We introduce a novel benchmark, StudentSyn, centered around the following challenge. For a given student, synthesize the student's attempt on a new target task after observing the student's attempt on a fixed reference task. This challenge is akin to that of program synthesis; however, instead of a solution (i.e., program an expert would write), the goal here is to synthesize a student attempt (i.e., program that a given student would write)
  arXiv  Detail & Related papers  (2022-05-03T01:32:47Z)
• Using Machine Learning to Predict Engineering Technology Students' Success with Computer Aided Design [50.591267188664666]
  We show how data combined with machine learning techniques can predict how well a particular student will perform in a design task. We found that our models using early design sequence actions are particularly valuable for prediction. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.
  arXiv  Detail & Related papers  (2021-08-12T20:24:54Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Learning abstract structure for drawing by efficient motor program induction [52.13961975752941]
  We develop a naturalistic drawing task to study how humans rapidly acquire structured prior knowledge. We show that people spontaneously learn abstract drawing procedures that support generalization. We propose a model of how learners can discover these reusable drawing programs.
  arXiv  Detail & Related papers  (2020-08-08T13:31:14Z)
• On the Nature of Programming Exercises [0.0]
  It is essential to understand that the nature of a programming exercise is an important factor for the success and consistent learning. This paper explores different approaches on the creation of a programming exercise.
  arXiv  Detail & Related papers  (2020-06-25T15:22:26Z)
• IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis [70.61283188380689]
  We investigate the problem of revealing the functionality of a black-box agent. We do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding an approximately functional equivalent program in 78% of cases.
  arXiv  Detail & Related papers  (2020-06-18T17:50:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.