Brett A. Becker
Daniel Gallagher
Paul Denny
James Prather
Garrett Powell
ABSTRACT
Humans adjust how they speak depending on context. Two key facets of this are utilizing different vocabulary and speaking rates depending on the audience. Exactly how we use language while teaching may depend on our students, their backgrounds and needs, and the subject matter. How we speak in the classroom likely affects student comprehension and may affect equity and accessibility.
We analyzed audio transcripts of three introductory programming courses delivered by different instructors at different institutions on three continents as well as several sessions of a popular online introduction to CS course. All used the same programming language (C) and had varying percentages of non-native English-speaking students. We investigated the vocabulary used and the rate of speech of each.
We found that many qualities of the instructional language used in these courses are remarkably similar. We did observe a striking difference in the rate of speech, a factor known to affect comprehension for non-native English speakers. These findings raise several questions about the speech we use in teaching. This is particularly relevant as the mode of delivery for many institutions is now entirely online, or involves recorded live lectures. These findings may also inform efforts to tailor delivery for non-native English speakers, students of different abilities, and pre-university students.
- Suad Alaofi. 2020. The Impact of English Language on Non-Native English Speaking Students' Performance in Programming Class. In Proc. 2020 ACM Conf Inn. Tech. CSE (Trondheim, Norway) (ITiCSE '20). ACM, NY, NY, USA, 585â 586.Google Scholar
Digital Library
- Suad Alaofi and Seán Russell. 2021. Computer Terminology Test for Non-Native English Speaking CS1 Students .ACM, NY, NY, USA, 1304.Google Scholar
- Keith Bain, Sara H. Basson, and Mike Wald. 2002. Speech Recognition in University Classrooms: Liberated Learning Project. In Proc. 5th Int. ACM Conf. on Assistive Tech. (Edinburgh, Scotland) (Assets '02). ACM, NY, NY, USA, 192196.Google ScholarDigital Library
- Brett A. Becker. 2019. Parlez-vous Java? Bonjour La Monde != Hello World: Barriers to Programming Language Acquisition for Non-Native English Speakers. In 30th Wkshp of the Psychology of Programming Interest Group - PPIG '19 (Newcastle, UK). bibinfonumpages13 pages.Google Scholar
- Brett A. Becker. 2021 a. The Roles of Computing Terminology in Non-Computing Disciplines. In Proc. 26th ACM Conf Inn. Tech. CSE (Virtual Event, Germany) (ITiCSE '21). ACM, NY, NY, USA, 659.Google Scholar
- Brett A. Becker. 2021 b. What Does Saying That 'Programming is Hard' Really Say, and about Whom? Commun. ACM , Vol. 64, 8 (Jul 2021), 2729.Google ScholarDigital Library
- Brett A. Becker, Paul Denny, Raymond Pettit, Durell Bouchard, Dennis J. Bouvier, Brian Harrington, Amir Kamil, Amey Karkare, Chris McDonald, Peter-Michael Osera, Janice L. Pearce, and James Prather. 2019. Compiler Error Messages Considered Unhelpful: The Landscape of Text-Based Programming Error Message Research. In Proc. WG Reports on Inn. and Tech. in CSE (Aberdeen, Scotland Uk) (ITiCSE-WGR '19). ACM, NY, NY, USA, 177210.Google ScholarDigital Library
- Brett A. Becker and Thomas Fitzpatrick. 2019. What Do CS1 Syllabi Reveal About Our Expectations of Introductory Programming Students?. In Proc. 50th ACM Tech Symp on CSE (Minneapolis, MN, USA) (SIGCSE '19). ACM, NY, NY, USA, 10111017.Google Scholar
- Brett A. Becker and Keith Quille. 2019. 50 Years of CS1 at SIGCSE: A Review of the Evolution of Introductory Programming Education Research. In Proc. 50th ACM Tech Symp on CSE (Minneapolis, MN, USA) (SIGCSE '19). ACM, NY, NY, USA, 338344.Google Scholar
- Mrwan Ben Idris and Hany Ammar. 2018. The Correlation Between Arabic Students' English Proficiency and Their Computer Programming Ability at the University Level. Int'l Journal of Managing Public Sector Information and Communication Technologies (IJMPICT) , Vol. 9, 1 (2018), 1--10.Google ScholarCross Ref
- Edward M. Bennett, Rollin P. Mayer, and Philip R. Bagley. 1961. Man-Machine Communication via Simplified English. Commun. ACM , Vol. 4, 5 (May 1961), 235236.Google Scholar
- Rens Bod. 2013. A New History of the Humanities: The Search For Principles and Patterns From Antiquity to the Present .Oxford University Press.Google Scholar
- Geoff Brindley and Helen Slatyer. 2002. Exploring Task Difficulty in ESL Listening Assessment. Language testing , Vol. 19, 4 (2002), 369--394.Google Scholar
- Craig Chaudron. 1988. Second Language Classrooms: Research on Teaching and Learning .Cambridge University Press, Cambridge, UK.Google Scholar
- K-12 Computer Science Framework Steering Committee. 2016. K-12 Computer Science Framework . Technical Report. NY, NY, USA.Google Scholar
- Peter J. Denning. 1989. A Debate on Teaching Computing Science. Commun. ACM , Vol. 32, 12 (Dec. 1989), 13971414.Google Scholar
- Paul Denny, James Prather, Brett A. Becker, Catherine Mooney, John Homer, Zachary C Albrecht, and Garrett B. Powell. 2021. On Designing Programming Error Messages for Novices: Readability and Its Constituent Factors. In Proc. 2021 CHI Conf. on Human Factors in Computing Systems. ACM, NY, NY, Article 55.Google Scholar
- Tracey M. Derwing. 1990. Speech Rate is No Simple Matter: Rate Adjustment and NSNNS Communicative Success. Studies in Second Language Acquisition , Vol. 12, 3 (1990), 303--313.Google ScholarCross Ref
- Ira Diethelm and Juliana Goschler. 2015. Questions on Spoken Language and Terminology for Teaching Computer Science. In Proc. 2015 ACM Conf Inn. Tech. CSE (Vilnius, Lithuania) (ITiCSE '15). ACM, NY, NY, USA, 2126.Google ScholarDigital Library
- William H DuBay. 2007. Smart Language: Readers, Readability, and the Grading of Text .ERIC.Google Scholar
- Martin R. Edwards and Michael E. Clinton. 2019. A Study Exploring the Impact of Lecture Capture Availability and Lecture Capture Usage on Student Attendance and Attainment. Higher Education , Vol. 77, 3 (2019), 403 -- 421.Google ScholarCross Ref
- Joanna Goode, Kirsten Peterson, and Gail Chapman. 2020. Online Professional Development for Computer Science Teachers: Gender-Inclusive Instructional Design Strategies. Int. J. of Gender, Science and Technology , Vol. 11, 3 (2020), 394--404.Google Scholar
- Roger Griffiths. 1990. Speech rate and NNS comprehension: A Preliminary Study in Time-benefit Analysis. Language Learning , Vol. 40, 3 (1990), 311--336.Google ScholarCross Ref
- Philip J. Guo. 2018. Non-Native English Speakers Learning Computer Programming: Barriers, Desires, and Design Opportunities. In Proc. 2018 CHI Conf. on Human Factors in Computing Systems (Montreal QC, Canada) (CHI '18). ACM, NY, NY, USA, 114.Google ScholarDigital Library
- Mark Guzdial. 2021. What Liberal Arts and Sciences Students Need to Know About Computing. https://cacm.acm.org/blogs/blog-cacm/249787-what-liberal-arts-and-sciences-students-need-to-know-about-computing/fulltextGoogle Scholar
- Evelyn Marcussen Hatch. 1983. Psycholinguistics: A Second Language Perspective .Newbury House, Rowley, MA.Google Scholar
- Matthew Hertz. 2010. What Do "CS1" and "CS2" Mean? Investigating Differences in the Early Courses. In Proc. 41st ACM Tech Symp on CSE (Milwaukee, WI, USA) (SIGCSE '10). ACM, NY, NY, USA, 199203.Google ScholarDigital Library
- Johannes Hofmeister, Janet Siegmund, and Daniel V Holt. 2017. Shorter Identifier Names Take Longer to Comprehend. In 2017 IEEE 24th Int'l Conf. on Software Analysis, Evolution and reengineering (SANER). IEEE, 217--227.Google Scholar
- Diane Horton and Michelle Craig. 2015. Drop, Fail, Pass, Continue: Persistence in CS1 and Beyond in Traditional and Inverted Delivery. In Proc. 46th ACM Tech Symp on CSE (Kansas City, MI, USA) (SIGCSE '15). ACM, NY, NY, USA, 235240.Google ScholarDigital Library
- Denis Howe. [n.d.]. Free Online Dictionary of Computing . https://foldoc.org/.Google Scholar
- Richard Kheir and Thomas Way. 2007. Inclusion of Deaf Students in Computer Science Classes Using Real-time Speech Transcription. In ITiCSE 2007. 261--265.Google ScholarDigital Library
- Tobias Kuhn. 2014. A Survey and Classification of Controlled Natural Languages. Comput. Linguist. , Vol. 40, 1 (March 2014), 121170.Google ScholarDigital Library
- Sander Lestrade. 2017. Unzipping Zipfs Law. PLOS ONE , Vol. 12, 8 (Aug 2017), 1--13.Google ScholarCross Ref
- Colleen M. Lewis, Joanna Goode, Allison Scott, Niral Shah, and Sepehr Vakil. 2020. Researching Race in Computer Science Education: Demystifying Key Vocabulary and Methods. In Proc. 51st ACM Tech Symp on CSE (Portland, OR, USA) (SIGCSE '20). ACM, NY, NY, USA, 171172.Google ScholarDigital Library
- Angel Lin. 2013. Classroom Code-switching: Three Decades of Research. Applied Linguistics Review , Vol. 4, 1 (2013), 195--218.Google ScholarCross Ref
- Andrew Manches, Peter E McKenna, Gnanathusharan Rajendran, and Judy Robertson. 2020. Identifying Embodied Metaphors for Computing Education. Computers in Human Behavior , Vol. 105 (2020), 105859.Google ScholarDigital Library
- Christopher Manning and Hinrich Schutze. 1999. Foundations of Statistical Natural Language Processing .MIT press.Google ScholarDigital Library
- Paola Medel and Vahab Pournaghshband. 2017. Eliminating Gender Bias in Computer Science Education Materials. In Proc. 48th ACM Tech Symp on CSE (Seattle, WA, USA) (SIGCSE '17). ACM, NY, NY, USA, 411416.Google ScholarDigital Library
- Craig S. Miller and Amber Settle. 2019. Learning to Get Literal: Investigating Reference-Point Difficulties in Novice Programming. ACM Trans. Comput. Educ. , Vol. 19, 3, Article 28 (May 2019), bibinfonumpages17 pages.Google Scholar
- Ruslan Mitkov. 2004. The Oxford Handbook of Computational Linguistics .Oxford University Press.Google ScholarDigital Library
- Neil P. Morris, Bronwen Swinnerton, and Taryn Coop. 2019. Lecture Recordings to Support Learning: A Contested Space Between Students and Teachers. Computers & Education , Vol. 140 (2019), 103604.Google ScholarDigital Library
- Brad A. Myers and Jeffrey Stylos. 2016. Improving API Usability. Commun. ACM , Vol. 59, 6 (May 2016), 6269.Google ScholarDigital Library
- Courtney Napoles and Mark Dredze. 2010. Learning Simple Wikipedia: A Cogitation in Ascertaining Abecedarian Language. In Proc. NAACL HLT 2010 Wkshp on Computational Linguistics and Writing: Writing Processes and Authoring Aids (LA, USA) (CL&W '10). Association for Comp. Linguistics, USA, 4250.Google Scholar
- Charles Kay Ogden. 1930. Basic English: A General Introduction with Rules and Grammar. (1930).Google Scholar
- Steven T Piantadosi. 2014. Zipfs Word Frequency Law in Natural Language: A Critical Review and Future Directions. Psych. Bull. & Rev. , Vol. 21, 5 (2014), 1112--1130.Google ScholarCross Ref
- Valerie Picardo, Paul Denny, and Andrew Luxton-Reilly. 2021. Lecture Recordings, Viewing Habits, and Performance in an Introductory Programming Course. In Australasian Computing Education Conf. ACM, NY, NY, USA, 7379.Google ScholarDigital Library
- Nelishia Pillay and Vikash R. Jugoo. 2005. An Investigation into Student Characteristics Affecting Novice Programming Performance. SIGCSE Bull. , Vol. 37, 4 (Dec. 2005), 107110.Google ScholarDigital Library
- David Pritchard. 2015. Frequency Distribution of Error Messages. In Proc. 6th Wkshp on Evaluation and Usability of Programming Languages and Tools (Pittsburgh, PA, USA) (PLATEAU 2015). ACM, NY, NY, USA, 18.Google ScholarDigital Library
- Rohit Ranchal, Teresa Taber-Doughty, Yiren Guo, Keith Bain, Heather Martin, J. Paul Robinson, and Bradley S. Duerstock. 2013. Using Speech Recognition for Real-time Captioning and Lecture Transcription in the Classroom. IEEE Transactions on Learning Technologies , Vol. 6, 4 (2013), 299--311.Google ScholarDigital Library
- Kyle Reestman and Brian Dorn. 2019. Native Language's Effect on Java Compiler Errors. In Proc. 2019 ACM Conf. on Int'l Computing Education Research (Toronto ON, Canada) (ICER '19). ACM, NY, NY, USA, 249257.Google ScholarDigital Library
- Kevin D Revell. 2014. A Comparison of the Usage of Tablet PC, Lecture Capture, and Online Homework in an Introductory Chemistry Course. Journal of Chemical Education , Vol. 91, 1 (2014), 48--51.Google ScholarCross Ref
- G. Salton and M. E. Lesk. 1965. The SMART Automatic Document Retrieval Systemsâ?"an Illustration. Commun. ACM , Vol. 8, 6 (June 1965), 391398.Google Scholar
- Joseph P. Sanford, Aaron Tietz, Saad Farooq, Samuel Guyer, and R. Benjamin Shapiro. 2014. Metaphors We Teach By. In Proc. 45th ACM Tech Symp on CSE (Atlanta, GA, USA) (SIGCSE '14). ACM, NY, NY, USA, 585590.Google Scholar
- Ayun Bekti Saparena, Galuh Kirana Dwi Areni, and Seful Bahri. 2018. Analysis of The Generic Structure of News Item on The Most Viewed Voice of America (VOA) Learning English Videos in February 2016. In ELT Forum: Journal of English Language Teaching, Vol. 7. 73--81.Google Scholar
- Lenhart Schubert. 2020. Computational Linguistics . In The Stanford Encyclopedia of Philosophy spring 2020 ed.), , Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University.Google Scholar
- Graham P Shaw and David Molnar. 2011. Non-native English Language Speakers Benefit Most From the Use of Lecture Capture in Medical School. Biochemistry and Molecular Biology Education , Vol. 39, 6 (2011), 416--420.Google ScholarCross Ref
- Adalbert Gerald Soosai Raj, Kasama Ketsuriyonk, Jignesh M. Patel, and Richard Halverson. 2018. Does Native Language Play a Role in Learning a Programming Language?. In Proc. 49th ACM Tech Symp on CSE (Baltimore, MD, USA) (SIGCSE '18). ACM, NY, NY, USA, 417422.Google ScholarDigital Library
- Andreas Stefik and Susanna Siebert. 2013. An Empirical Investigation into Programming Language Syntax. ACM Trans. Comput. Educ. , Vol. 13, 4, Article 19 (Nov. 2013), bibinfonumpages40 pages.Google ScholarDigital Library
- Verena Stein, Robert Neßelrath, Jan Alexandersson, and Johannes Tröger. 2011. Designing with and for the Visually Impaired: Vocabulary, Spelling and the Screen Reader.. In CSEDU (2). 462--467.Google Scholar
- Ashok Veerasamy and Anna Shillabeer. 2014. Teaching English Based Programming Courses to English Language Learners/Non-Native Speakers of English. In Int'l Proc. of Economics Development and Research, Vol. 70. 17--22.Google Scholar
- Priscilla Fawn Whittaker and Junsheng Wang. 1997. The Voice of America: Moving ESL Students Toward Listening Success. TESL Reporter , Vol. 30 (1997), 3--3.Google Scholar
- Judith D. Wilson. 1987. Entity-Relationship Diagrams and English: An Analysis of Some Problems Encountered in a Database Design Course. In Proc. 18th ACM Tech Symp on CSE (St. Louis, MI, USA) (SIGCSE '87). ACM, NY, NY, USA, 26--35.Google ScholarDigital Library
Index Terms
- From the Horse's Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents
Recommendations
Investigating the Role of Different Prep Pathways on CS2 Performance Across Three Different Majors
SIGITE '21: Proceedings of the 22nd Annual Conference on Information Technology EducationResearch have shown that introductory programming sequence have a significant impact in the retention of students in computing and engineering majors. There has been extensive research about the CS1 course [1]. Much less has been written about the CS1.5 ...
The Lombard intelligibility benefit of native and non-native speech for native and non-native listeners
Highlights- We compared native English and non-native (Dutch) Lombard and plain speech.
- ...
AbstractSpeech produced in noise (Lombard speech) is more intelligible than speech produced in quiet (plain speech). Previous research on the Lombard intelligibility benefit focused almost entirely on how native speakers produce and perceive ...
Perceived accentedness and intelligibility: The relative contributions of F0 and duration
The current study sought to determine the relative contributions of suprasegmental and segmental features to the perception of foreign accent and intelligibility in both first language (L1) and second language (L2) German and English speech. ...
Comments