ABSTRACT
Email is an important mode of communication because it scales to the largest computing courses and is institutionally supported. Furthermore, regular email communication from instructors has been shown to help set student expectations and encourage participation. As a result, effective email can contribute to emotional engagement, which has been linked to improvements in performance and retention, the latter being a persistent problem in computer science. However, we lack a clear picture of how computing students interact with emails and whether their use aligns with instructors’ expectations. This paper addresses this gap by presenting data on how often CS1 students open instructor emails. We present email engagement data throughout the term for a particular type of email that prompts students to plan to start their homework. Contrary to instructors’ expectations, the rate at which students open emails of this kind does not change significantly over the term. Many students who engage with the emails do so consistently, even after repeated emails throughout the term. The patterns we found illustrate the value of collecting this type of data and informing instructors and researchers about who reads these messages and how often they actually reach students.
- 2023. Mail merge for Gmail - yet another mail merge. https://yamm.com/Google Scholar
- Bshaer Alwagdani, Khalid Alomar, Saudi Arabia, and Khalid Alomar. 2020. Increasing Student Engagement with Personalized Emails. Computer and Information Science 13, 2 (2020), 1–54.Google ScholarCross Ref
- Apple. 2021. Apple advances its privacy leadership with IOS 15, ipados 15, macOS Monterey, and watchos 8. Apple Newsroom (Jun 2021). https://www.apple.com/newsroom/2021/06/apple-advances-its-privacy-leadership-with-ios-15-ipados-15-macos-monterey-and-watchos-8/Google Scholar
- Katherine D Arbuthnott. 2009. Education for sustainable development beyond attitude change. International Journal of Sustainability in Higher Education (2009).Google ScholarCross Ref
- Yougan Aungamuthu. 2011. Email messages: Towards a pedagogy of caring. The Journal of Independent Teaching and Learning 6, 1 (2011), 34–44.Google Scholar
- Ryan Shaun Baker, Albert T Corbett, Kenneth R Koedinger, and Angela Z Wagner. 2004. Off-task behavior in the cognitive tutor classroom: When students" game the system". In Proceedings of the SIGCHI conference on Human factors in computing systems. 383–390.Google ScholarDigital Library
- Jens Bennedsen and Michael E Caspersen. 2007. Failure rates in introductory programming. AcM SIGcSE Bulletin 39, 2 (2007), 32–36.Google ScholarDigital Library
- Jens Bennedsen and Michael E Caspersen. 2019. Failure rates in introductory programming: 12 years later. ACM inroads 10, 2 (2019), 30–36.Google ScholarDigital Library
- Maureen Biggers, Anne Brauer, and Tuba Yilmaz. 2008. Student perceptions of computer science: a retention study comparing graduating seniors with cs leavers. ACM SIGCSE Bulletin 40, 1 (2008), 402–406.Google ScholarDigital Library
- Catherine A Bliss and Betty Lawrence. 2009. From posts to patterns: A metric to characterize discussion board activity in online courses.Journal of Asynchronous Learning Networks 13, 2 (2009), 15–32.Google Scholar
- Doris U Bolliger and Florence Martin. 2018. Instructor and student perceptions of online student engagement strategies. Distance Education 39, 4 (2018), 568–583.Google ScholarCross Ref
- Melissa Bond, Katja Buntins, Svenja Bedenlier, Olaf Zawacki-Richter, and Michael Kerres. 2020. Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International journal of educational technology in higher education 17, 1 (2020), 1–30.Google ScholarCross Ref
- André Bonfrer and Xavier Drèze. 2009. Real-time evaluation of e-mail campaign performance. Marketing Science 28, 2 (2009), 251–263.Google ScholarDigital Library
- Matthew Butler, Michael Morgan, Judy Sheard, Katrina Falkner, and Amali Weerasinghe. 2015. Initiatives to increase engagement in first-year ICT. In Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education. 308–313.Google ScholarDigital Library
- Ching-Wen Chang, Beth Hurst, and Annice McLean. 2015. You’ve got mail: Student preferences of instructor communication in online courses in an age of advancing technologies. Journal of Educational Technology Development and Exchange (JETDE) 8, 1 (2015), 3.Google Scholar
- Moon-Heum Cho and YoonJung Cho. 2014. Instructor scaffolding for interaction and students’ academic engagement in online learning: Mediating role of perceived online class goal structures. The Internet and Higher Education 21 (2014), 25–30.Google ScholarCross Ref
- Andreia Conceição and João Gama. 2019. Main factors driving the open rate of email marketing campaigns. In Discovery Science: 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings 22. Springer, 145–154.Google ScholarDigital Library
- Kristen Cuthrell and Anna Lyon. 2007. Instructional strategies: What do online students prefer. Journal of Online Learning and Teaching 3, 4 (2007), 357–362.Google Scholar
- Sarah Dart and Belinda Spratt. 2020. Personalised emails in first-year mathematics: Exploring a scalable strategy for improving student experiences and outcomes. Student Success 11, 2 (2020), 1–12.Google Scholar
- Vanessa P Dennen, A Aubteen Darabi, and Linda J Smith. 2007. Instructor–learner interaction in online courses: The relative perceived importance of particular instructor actions on performance and satisfaction. Distance education 28, 1 (2007), 65–79.Google Scholar
- Marcia D Dixson. 2010. Creating effective student engagement in online courses: What do students find engaging?Journal of the Scholarship of Teaching and Learning (2010), 1–13.Google Scholar
- Stephen H Edwards, Jason Snyder, Manuel A Pérez-Quiñones, Anthony Allevato, Dongkwan Kim, and Betsy Tretola. 2009. Comparing effective and ineffective behaviors of student programmers. In Proceedings of the fifth international workshop on Computing education research workshop. 3–14.Google ScholarDigital Library
- Ruth B Ekstrom, Margaret E Goertz, Judith M Pollack, and Donald A Rock. 1986. Who drops out of high school and why? Findings from a national study. Teachers college record 87, 3 (1986), 356–373.Google Scholar
- Benjamin Fabian, Benedict Bender, and Lars Weimann. 2015. E-Mail Tracking in Online Marketing: Methods, Detection, and Usage.Google Scholar
- Katrina Falkner, Rebecca Vivian, and Nickolas JG Falkner. 2014. Identifying computer science self-regulated learning strategies. In Proceedings of the 2014 conference on Innovation & technology in computer science education. 291–296.Google ScholarDigital Library
- Jeremy D Finn. 1989. Withdrawing from school. Review of educational research 59, 2 (1989), 117–142.Google Scholar
- Jennifer A Fredricks, Phyllis C Blumenfeld, and Alison H Paris. 2004. School engagement: Potential of the concept, state of the evidence. Review of educational research 74, 1 (2004), 59–109.Google Scholar
- Jennifer A Fredricks, Michael Filsecker, and Michael A Lawson. 2016. Student engagement, context, and adjustment: Addressing definitional, measurement, and methodological issues., 4 pages.Google Scholar
- GetResponse. [n. d.]. Email marketing benchmarks (2020) by GetResponse. https://www.getresponse.com/resources/reports/email-marketing-benchmarksGoogle Scholar
- Peter M Gollwitzer and Paschal Sheeran. 2006. Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in experimental social psychology 38 (2006), 69–119.Google Scholar
- Elkafi Hassini. 2006. Student–instructor communication: The role of email. Computers & Education 47, 1 (2006), 29–40. https://doi.org/10.1016/j.compedu.2004.08.014Google ScholarDigital Library
- Géraldine Heilporn, Sawsen Lakhal, and Marilou Bélisle. 2021. An examination of teachers’ strategies to foster student engagement in blended learning in higher education. International Journal of Educational Technology in Higher Education 18 (2021), 1–25.Google ScholarCross Ref
- Diane Horton and Michelle Craig. 2015. Drop, fail, pass, continue: Persistence in CS1 and beyond in traditional and inverted delivery. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education. 235–240.Google ScholarDigital Library
- Geetha Kanaparan, Rowena Cullen, David Mason, 2019. Effect of self-efficacy and emotional engagement on introductory programming students. Australasian Journal of Information Systems 23 (2019).Google Scholar
- Stephanie B King. 2014. Graduate student perceptions of the use of online course tools to support engagement.International Journal for the Scholarship of Teaching & Learning 8, 1 (2014).Google Scholar
- Päivi Kinnunen and Lauri Malmi. 2006. Why students drop out CS1 course?. In Proceedings of the Second International Workshop on Computing Education Research. 97–108.Google ScholarDigital Library
- Susan Ko and Steve Rossen. 2017. Teaching online: A practical guide. Routledge.Google Scholar
- Kostadin Kushlev and Elizabeth W Dunn. 2015. Checking email less frequently reduces stress. Computers in Human Behavior 43 (2015), 220–228.Google ScholarDigital Library
- Soohyun Nam Liao, Kartik Shah, William G Griswold, and Leo Porter. 2021. A Quantitative Analysis of Study Habits Among Lower-and Higher-Performing Students in CS1. In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1. 366–372.Google ScholarDigital Library
- Florence Martin and Doris U Bolliger. 2018. Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment.Online learning 22, 1 (2018), 205–222.Google Scholar
- Joshua Martin, Stephen H Edwards, and Clfford A Shaffer. 2015. The effects of procrastination interventions on programming project success. In Proceedings of the eleventh annual International Conference on International Computing Education Research. 3–11.Google ScholarDigital Library
- Jianyang Mei. 2016. Learning Management System Calendar Reminders and Effects on Time Management and Academic Performance.International Research and Review 6, 1 (2016), 29–45.Google Scholar
- Nicolas Michinov, Sophie Brunot, Olivier Le Bohec, Jacques Juhel, and Marine Delaval. 2011. Procrastination, participation, and performance in online learning environments. Computers & Education 56, 1 (2011), 243–252. https://doi.org/10.1016/j.compedu.2010.07.025 Serious Games.Google ScholarDigital Library
- Alison K Miller, Alexander J Rothman, and Richie L Lenne. 2022. What’s said in a subject line? Framing the email subject lines in health messages sent to university students. Journal of American College Health 70, 2 (2022), 446–452.Google ScholarCross Ref
- Michael Morgan, Matthew Butler, Jane Sinclair, Christabel Gonsalvez, and Neena Thota. 2018. Contrasting CS student and academic perspectives and experiences of student engagement. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. 1–35.Google Scholar
- Michael Morgan, Matthew Butler, Neena Thota, and Jane Sinclair. 2018. How CS academics view student engagement. In Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. 284–289.Google ScholarDigital Library
- Donald E Mowrer. 1996. A content analysis of student/instructor communication via computer conferencing. Higher Education 32, 2 (1996), 217–241.Google ScholarCross Ref
- Dip Nandi, Margaret Hamilton, James Harland, and Geoff Warburton. 2011. How active are students in online discussion forums?. In Proceedings of the Thirteenth Australasian Computing Education Conference-Volume 114. 125–134.Google ScholarDigital Library
- Iryna Nikolayeva, Amel Yessad, Bertrand Laforge, and Vanda Luengo. 2020. Does an e-mail reminder intervention with learning analytics reduce procrastination in a blended university course?Addressing Global Challenges and Quality Education (2020), 60–73. https://doi.org/10.1007/978-3-030-57717-9_5Google ScholarDigital Library
- Andrew Petersen, Michelle Craig, Jennifer Campbell, and Anya Tafliovich. 2016. Revisiting Why Students Drop CS1. In Proceedings of the 16th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling ’16). Association for Computing Machinery, New York, NY, USA, 71–80. https://doi.org/10.1145/2999541.2999552Google ScholarDigital Library
- Maura Pilotti, Stephanie Anderson, Pamela Hardy, Pamela Murphy, and Pamela Vincent. 2017. Factors related to cognitive, emotional, and behavioral engagement in the online asynchronous classroom.International Journal of Teaching and Learning in Higher Education 29, 1 (2017), 145–153.Google Scholar
- Navdeep S. Sahni, S. Christian Wheeler, and Pradeep Chintagunta. 2018. Personalization in email marketing: The role of Noninformative Advertising Content. Marketing Science 37, 2 (2018), 236–258. https://doi.org/10.1287/mksc.2017.1066Google ScholarDigital Library
- Clifford A Shaffer and Stephen H Edwards. 2011. Scheduling and student performance. In Proceedings of the 16th annual joint conference on innovation and technology in computer science education. 331–331.Google ScholarDigital Library
- Judy Sheard, Angela Carbone, Donald Chinn, and Mikko-Jussi Laakso. 2013. Study habits of CS 1 students: What do they say they do?. In 2013 Learning and Teaching in Computing and Engineering. IEEE, 122–129.Google Scholar
- Vivian C Sheer and Timothy K Fung. 2007. Can email communication enhance professor-student relationship and student evaluation of professor?: Some empirical evidence. Journal of Educational Computing Research 37, 3 (2007), 289–306.Google ScholarCross Ref
- Manoj Souza, Paul Rodrigues, 2015. Investigating the effectiveness of the flipped classroom in an introductory programming course. The New Educational Review 40, 1 (2015), 129–139.Google ScholarCross Ref
- Chris Stephenson, Alison Derbenwick Miller, Christine Alvarado, Lecia Barker, Valerie Barr, Tracy Camp, Carol Frieze, Colleen Lewis, Erin Cannon Mindell, Lee Limbird, 2018. Retention in computer science undergraduate programs in the us: Data challenges and promising interventions. ACM.Google Scholar
- Salla Willman, Rolf Lindén, Erkki Kaila, Teemu Rajala, Mikko-Jussi Laakso, and Tapio Salakoski. 2015. On study habits on an introductory course on programming. Computer Science Education 25, 3 (2015), 276–291.Google ScholarCross Ref
- Stacy Young, Dawn Kelsey, and Alexander Lancaster. 2011. Predicted outcome value of e-mail communication: Factors that foster professional relational development between students and teachers. Communication Education 60, 4 (2011), 371–388.Google ScholarCross Ref
- Jianhui Yu, Changqin Huang, Xizhe Wang, and Yaxin Tu. 2020. Exploring the relationships among interaction, emotional engagement and learning persistence in online learning environments. In 2020 International Symposium on Educational Technology (ISET). IEEE, 293–297.Google ScholarCross Ref
- Angela Zavaleta-Bernuy, Ziwen Han, Hammad Shaikh, Qi Yin Zheng, Lisa-Angelique Lim, Anna Rafferty, Andrew Petersen, and Joseph Jay Williams. 2022. How can Email Interventions Increase Students’ Completion of Online Homework? A Case Study Using A/B Comparisons. In LAK22: 12th International Learning Analytics and Knowledge Conference.Google ScholarDigital Library
Index Terms
- Do Students Read Instructor Emails? A Case Study of Intervention Email Open Rates
Recommendations
Student Interaction with Instructor Emails in Introductory and Upper-Year Computing Courses
SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1In computing courses, instructor involvement and social comfort are vital for resilience and belonging. We examine engagement with instructor emails aimed at strengthening the connection with students. We sent weekly emails from instructors to first- and ...
Investigating Subject Lines Length on Students' Email Open Rates
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2Instructors often prefer to use email for course communication. The use of emails has been widely discussed in the fields of marketing and behavioural design, but the prevalence of email in education makes it important for instructors to collect metrics ...
Status Update on Phishing Emails Awareness: Jordanian Case
ICEMIS'21: The 7th International Conference on Engineering & MIS 2021Abstract—This study is a response to the rapid proliferation of high-risk phishing emails, representing one of the most dangerous cybercrimes and the primary medium for the deception of online users. This study aims to investigate the various ...
Comments