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The rise of the zyLab program auto-grader in introductory CS courses

Read the Full Paper by C. Gordon, R. Lysecky, and F. Vahid.

Abstract

In recent years, hundreds of college courses have switched how they grade programming assignments, from grading manually and/or using batch scripts, to using commercial cloud-based auto-graders with immediate score feedback to students. This white paper provides data on the rise in usage of one of the most widely-used program auto-graders, zyLabs, as one indicator of the strong shift in college course grading to the auto-grading paradigm. The number of courses, instructors and students using zyLabs have increased dramatically since it was first introduced, such that from 2016 to 2021, the number of courses per year grew from 284 to 3,935, the number of students per year from 24,216 in 2016 to 220,453 in 2021, and the number of instructors per year from 364 to 3,724. The result is a substantial shift in the classroom dynamic that enables instructors and students to spend more time on quality teaching and learning.

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Theory to Practice: Closing the Gap in Undergraduate Math to Reduce Student Attrition

Read the Full Paper by J. Kelly, J. Bruno, and A. Edgcomb.

Abstract

Researchers have developed numerous effective methods and theoretical models for teaching undergraduate general education mathematics (UGEM); however, many universities have struggled for decades to bridge the translational gap in courses. This gap has contributed to the national lack of a skilled STEM workforce. Recently, University of Phoenix has decided to close the gap in translating theory into practice by shifting the institution’s philosophical framework about math education from traditionalist methodology to a synthesis of seminal theories and best practices. The purpose of this paper is to disseminate the implementation of theory-based practices in UGEM toward reducing student attrition, including: Rationale for theory identification, the construction of a philosophical framework for course implementation, collection of stakeholder input, implementation, evaluation of the impact on attrition, post-implementation maintenance and communication, and institutional socialization of the new paradigmatic shift. These efforts yielded an attrition rate reduction from 17.5% to 4.7% of students withdrawing or failing in Quantitative Reasoning 1 and from 13.9% to 4.0% in Quantitative Reasoning 2.

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The Shift From Static College Textbooks to Customizable Content: A Case Study at zyBooks

Read the Full Paper by C. Gordon, R. Lysecky, and F. Vahid.

College textbook publishing is transforming from a model of static textbooks to a modern model of customizable textbooks. Customization may involve reconfiguring content, combining textbooks, authoring one’s own content, adding notes to content, and more. As such, publishing is moving away from a model of selling static textbooks, and toward a model of providing a library of content from which instructors can build a course. This paper provides data for one digital-only publisher, zyBooks, on the prevalence and trends around reconfiguring textbooks, combining textbooks, instructor-authored sections, and instructor-added notes. The data show that for over 4,000 classes in 2020, over 85% of classes reconfigured their books, over 30% of classes combined two or more books with hundreds combining three or more, about 30% of books had instructor notes added, and about 65% of zyLabs-enabled zyBooks included instructor-created labs. The trend away from static textbooks and toward customizable content has substantial implications on how content is authored, requiring more modularity of content sections to support reconfiguration, and requiring more consistency across subjects to enable combining content. The trend also has substantial implications on book marketing, pricing, renewals, and more.

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Impact of Several Low-Effort Cheating-Reduction Methods in a CS1 Class

Read the Full Paper by F. Vahid, A. Pang, K. Downey, and C. Gordon.

Abstract

Cheating in introductory programming classes (CS1) is a well-known problem. Many instructors seek methods to prevent such cheating. Some methods are time-consuming or don’t scale to large classes. We experimented with several low-effort commonly-suggested methods to reduce cheating: (1) Discussing academic integrity for 20-30 minutes, several weeks into the term, (2) Requiring an integrity quiz with explicit do’s and don’ts, (3) Allowing students to withdraw program submissions, (4) Reminding students mid-term about integrity and consequences of getting caught, (5) Showing tools in class that an instructor has available (including a similarity checker, statistics on time spent, and access to a student’s full coding history), (6) Normalizing help and pointing students to help resources. Because counting students actually caught cheating is not objective (being influenced by how much effort is spent in detecting and investigating, and how an instructor subjectively decides cheating has occurred), we developed two automated coding-behavior metrics that may suggest how much cheating is happening. We compared those metrics for terms before and after the intervention. The results show substantial student behavior improvements when applying those low-effort methods. In our Fall 2021 comparison, time spent programming increased from 6 min 56 sec, to 11 min 6 sec, for a 60% increase. And, the percent of students with suspiciously similar programs dropped from 33% to 18%, for a 45% decrease.

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