skip to main content
10.1145/3372356.3372370acmotherconferencesArticle/Chapter ViewAbstractPublication PagescepConference Proceedingsconference-collections
short-paper

Addressing mixed levels of prior knowledge by individualising learning pathways in a Degree Apprenticeship Summer School

Published: 10 January 2020 Publication History

Abstract

Teaching an introductory programming course is beset by two core challenges: students enter the course with different levels of prior experience; and, for whatever reason, they progress at different rates. This is at odds with a typical face-to-face course, where material is delivered to the whole class in a lock-step fashion. Addressing these two challenges lies at the heart of an eight-week Summer School, described here, designed to bring a broad intake of students up to a common level ready to start on an degree apprenticeship programme that assumes a certain level of programming ability at the outset.
A programming test of students' understanding of programming constructs (e.g. conditional logic, loops, methods etc) allowed us to select for whom the course was mandatory, but it was open to all. Successful completion of the Summer School required students to demonstrate mastery of nine competencies. Each competency had an initial test, if the student did not meet the required level they were asked to complete coursework using Jupyter Notebooks. If the student passed the initial test they moved straight to the final test and if they passed, they moved to the next competency. There were also practice quizzes for students to do before the final test. This provided an individualised learning pathway for the students.
Students improved in the programming test and have given favourable reviews of the Summer School in a focus group.

References

[1]
Matthew Barr and Jack Parkinson. 2019. Developing a Work-based Software Engineering Degree in Collaboration with Industry. UKICER Proceedings of the 1st UK Ireland Computing Education Research Conference 9 (2019).
[2]
Finland Helsinki. 2000. Does it help to have some programming experience before beginning a computing degree program? ACM SIGCSE Bulletin 32, 3 (2000), 25--28.
[3]
Phil Robbins Raymond Lister Mike Lopez, Jacqueline Whalley. 2008. Relationships between reading, tracing and writing skills in introductory programming. ICER '08 Proceedings of the Fourth international Workshop on Computing Education Research (2008), 101--112.
[4]
Mark Guzdial Shelly Engleman Miranda C. Parker. 2016. Replication, Validation, and Use of a Language Independent CS1 Knowledge Assessment. (English). ICER '16 Proceedings of the 2016 ACM Conference on International Computing Education Research (2016), 93--101.
[5]
Jack Parkinson and Quintin Cutts. 2018. Investigating the Relationship Between Spatial Skills and Computer Science. ICER '18 Proceedings of the 2018 ACM Conference on International Computing Education Research (2018).
[6]
Jonathan Gratch Mohsen Dorodchi Ryan Bockmon, Stephen Cooper. 2019. (Re)Validating Cognitive Introductory Computing Instruments. (English). SIGCSE '19 Proceedings of the 50th ACM Technical Symposium on Computer Science Education (2019), 552--557.
[7]
Dechawut Wanichsan Parames Laosinchai Sasithorn Chookaew, Patcharin Panjaburee. 2014. A Personalized E-Learning Environment to Promote Studentsfi Conceptual Learning on Basic Computer Programming. Social and Behavioral Sciences 116 (2014).
[8]
Carsten Schulte. 2008. Block Model fi?! an Educational Model of Program Comprehension as a Tool for a Scholarly Approach to Teaching. Proceeding of the Fourth international Workshop on Computing Education Research (2008), 149--160.
[9]
Ricky Baker E A. Unger. 1983. A predictor for success in an introductory programming class based upon abstract reasoning development. (English). ACM SIGCSE Bulletin - Proceedings of the 14th SIGCSE technical symposium on Computer science education 15, 1 (1983), 154--158.
[10]
So Yoon Yoon. 2011. Psychometric properties of the Revised Purdue Spatial Visualization Tests: Visualization of Rotations (the Revised PSVT:R). Ph.D. Dissertation. Purdue University.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
CEP '20: Proceedings of the 4th Conference on Computing Education Practice
January 2020
69 pages
ISBN:9781450377294
DOI:10.1145/3372356
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 January 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Degree Apprenticeship
  2. Graduate Apprenticeship
  3. Introduction
  4. Programming
  5. Python

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

CEP 2020
CEP 2020: Computing Education Practice 2020
January 9, 2020
Durham, United Kingdom

Acceptance Rates

CEP '20 Paper Acceptance Rate 16 of 38 submissions, 42%;
Overall Acceptance Rate 32 of 71 submissions, 45%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 163
    Total Downloads
  • Downloads (Last 12 months)9
  • Downloads (Last 6 weeks)0
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media