Examining the use of Monitoring Applications in K-12 Classrooms: Perceptions of Students,Teachers,Technology Facilitators and Technology Directors

Abstract

Monitoring applications (MAs) software provides filtering and monitoring for technology management. In the fall of 2020, a southeastern state encouraged public and charter K-12 schools to use MAs by providing funding for their purchase. This was intended to protect students from online threats and self-harm during the COVID-19 pandemic, which might increase due to the swell of online classes. This study examined the perceptions of high school students, teachers, technology facilitators, and technology directors on the MAs used in their schools and districts during and as a result of the COVID-19 pandemic. Inferential statistics modeling was used to analyze Likert scale answer results from 146 survey respondents. Open-ended answers were coded on semantic themes. Prominent differences amongst groups arose in perceptions towards MA prevention capabilities, and teachers exhibited differences in awareness of MA capabilities related to whether they received training. Implications for better MA design and implementation to fulfill intended purposes are discussed.

Keywords

monitoring applications K-12 education cyberbullying online learning online safety

Get full access to this article

View all access options for this article.

References

Abraham

Niessen

Schnabel

Lorek

Grimm

Möslein

Wrede

(2019). Electronic monitoring at work: The role of attitudes, functions, and perceived control for the acceptance of tracking technologies. Human Resource Management Journal, 29(4), 657–675. https://doi.org/10.1111/1748-8583.12250

Abrahams

D. A.

(2010). Technology adoption in higher education: A framework for identifying and prioritising issues and barriers to adoption of instructional technology. Journal of Applied Research in Higher Education, 2(2), 34–49. https://doi.org/10.1108/17581184201000012

Annan

D. L.

(2008). Facilitating adoption of technology in higher education. Distance Learning, 5(1), 13–17. https://www.proquest.com/scholarly-journals/facilitating-adoption-technology-higher-education/docview/230727053/se-2

Arnon

Brunstein Klomek

Visoki

Moore

T. M.

Argabright

S. T.

DiDomenico

G. E.

Benton

T. D.

Barzilay

(2022). Association of cyberbullying experiences and perpetration with suicidality in early adolescence. JAMA Network Open, 5(6), e2218746. https://doi.org/10.1001/jamanetworkopen.2022.18746

Bauman

Newman

M. L.

(2013). Testing assumptions about cyberbullying: Perceived distress associated with acts of conventional and cyber bullying. Psychology of Violence, 3(1), 27–38. https://doi-org.prox.lib.ncsu.edu/10.1037/a0029867

Brown

C. F.

Demaray

M. K.

Secord

S. M.

(2014). Cyber victimization in middle school and relations to social emotional outcomes. Computers in Human Behavior, 35, 12–21. https://doi.org/10.1016/j.chb.2014.02.014

Burleigh

C. L.

Wilson

A. M.

(2022). Mobile technology acceptable use policies and teaching in high school classrooms: Do boundaries exist?. International Journal of Multidisciplinary and Current Educational Research, 4(1), 71–86.

Byars

Graybill

Wellons

Harper

(2020). Monitoring social media and technology use to prevent youth suicide and school violence. Contemporary School Psychology, 24(3), 318–326. https://doi.org/10.1007/s40688-020-00277-x

Camerini

A.-L.

Marciano

Carrara

Schulz

P. J.

(2020). Cyberbullying perpetration and victimization among children and adolescents: A systematic review of longitudinal studies. Telematics and Informatics, 49, 101362. https://doi.org/10.1016/j.tele.2020.101362

10.

Carli

Durkee

Wasserman

Hadlaczky

Despalins

Kramarz

Wasserman

Sarchiapone

Hoven

C. W.

Brunner

Kaess

(2013). The association between pathological internet use and comorbid psychopathology: A systematic review. Psychopathology, 46(1), 1–13. https://doi.org/10.1159/000337971

11.

Collins

Park

Reddy

Sharifi

Vance

(2021). The privacy and equity implications of using self-harm monitoring technologies: Recommendations for schools. Future of Privacy Forum. https://studentprivacycompass.org/wp-content/uploads/2021/09/FPF-Self-Harm-Report-R4.pdf

12.

Fedders

(2019). The constant and expanding classroom: Surveillance in K-12 public schools. North Carolina Law Review, 97, 1673. https://scholarship.law.unc.edu/nclr/vol97/iss6/4

13.

Fenton

(2021). Preparing elementary preservice teachers to integrate technology: Examining the effects of a new science course sequence with technology infusion. Electronic Journal for Research in Science & Mathematics Education, 26(1), 23–32.

14.

Fogarty

Brown

Gartland

Mensah

Seymour

Savopoulos

FitzPatrick

Papadopoullos

Giallo

(2022). Psychosocial factors associated with adolescent depressive and anxiety symptoms during the COVID-19 pandemic. International Journal of Behavioral Development, 46(4), 308–319. https://doi.org/10.1177/01650254221084100

15.

Fredrick

S. S.

Coyle

King

(2023). Middle and high school teachers’ perceptions of cyberbullying prevention and digital citizenship. Psychology in the Schools, 60(6), 1958–1978. https://doi.org/10.1002/pits.22844

16.

Gaggle. (2024). Gaggle | K-12 online safety management software. Gaggle. https://www.gaggle.net

17.

Gillum

Kao

(2019, June 25). The unproven, invasive surveillance technology schools are using to monitor students. ProPublica. https://features.propublica.org/aggression-detector/the-unproven-invasive-surveillance-technology-schools-are-using-to-monitor-students/

18.

GoGuardian. (2024). GoGuardian | engaging digital learning for schools. Goguardian. https://www.goguardian.com/

19.

Gopika

Rekha

(2023). MOOC Platforms in Asian countries: A comparative study of interest and adoption before and during COVID-19 pandemic. Indian Journal of Open Learning (ISSN: 0971-2690), 32(1), 3p–3p.

20.

Gorzig

(2016). Adolescents’ experience of offline and online risks: Separate and joint propensities. Computers in Human Behavior, 56, 9–13. https://doi.org/10.1016/j.chb.2015.11.006

21.

Grant-Chapman

Laird

Venzke

(2021). Student activity monitoring software: Research insights and recommendations (pp. 1–7). Center for Democracy & Technology.

22.

Hadjithoma-Garstka

(2011). The role of the principal’s leadership style in the implementation of ICT policy. British Journal of Educational Technology, 42(2), 311–326. https://doi.org/10.1111/j.1467-8535.2009.01014.x

23.

Hankerson

Venske

Laird

Grant-Chapman

Thakur

(2021). Online and observed: Student privacy implications of school-issued devices and student activity monitoring software. Center for Democracy & Technology. https://cdt.org/insights/report-online-and-observed-student-privacy-implications-of-school-issued-devices-and-student-activity-monitoring-software/

24.

Hinduja

Patchin

(2023). Cyberbullying identification, prevention, and response. Cyberbullying Research Center.

25.

Jevremovic

Veinovic

Cabarkapa

Krstic

Chorbev

Dimitrovski

Garcia

Pombo

Stojmenovic

(2021). Keeping children safe online with limited resources: Analyzing what is seen and heard. IEEE Access, 9, 132723–132732. https://doi.org/10.1109/ACCESS.2021.3114389

26.

Jonas

Salazar De Pablo

Shum

Nosarti

Abbott

Vaquerizo-Serrano

(2022). A systematic review and meta-analysis investigating the impact of childhood adversities on the mental health of LGBT+ youth. JCPP Advances, 2(2), e12079. https://doi.org/10.1002/jcv2.12079

27.

Jones

K. M. L.

Asher

Goben

Perry

M. R.

Salo

Briney

K. A.

Robertshaw

M. B.

(2020). “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology, 71(9), 1044–1059. https://doi.org/10.1002/asi.24358

28.

Kao

Gillum

(2020). Reverse-Engineering an Audio Aggression Detection Algorithm .

29.

Kikerpill

Siibak

(2023). Schools engaged in doom-monitoring students’ online interactions and content creation: An analysis of dominant media discourses. Child and Adolescent Mental Health, 28(1), 76–82. https://doi.org/10.1111/camh.12621

30.

Kim

J. Y.

(2012). The nonlinear association between internet using time for non-educational purposes and adolescent health. Journal of Preventive Medicine & Public Health, 45(1), 37–46. https://doi.org/10.3961/jpmph.2012.45.1.37

31.

Kruskal

W. H.

Wallis

W. A.

(1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.1080/01621459.1952.10483441

32.

Laird

Grant-Chapman

Venzke

Quay-de la Vallee

(2022). Hidden harms: The misleading promise of monitoring students online. Center for Democracy and Technology, https://cdt.org/wp-content/uploads/2022/08/Hidden-Harms-The-Misleading-Promise-of-Monitoring-Students-Online-Research-Report-Final-Accessible.pdf

33.

Levene

(1960). Robust tests for equality of variances. In Olkin

(Ed.), Contributions to probability and statistics (pp. 278–292). Stanford University Press.

34.

Livingstone

Stoilova

(2021). The 4Cs: Classifying Online Risk to Children. CO:RE Short Report Series on Key Topics. https://doi.org/10.21241/SSOAR.71817

35.

Llorent

V. J.

Ortega-Ruiz

Zych

(2016). Bullying and cyberbullying in minorities: Are they more vulnerable than the majority group? Frontiers in Psychology, 7(1507). https://doi.org/10.3389/fpsyg.2016.01507

36.

Mann

H. B.

Whitney

D. R.

(1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491

37.

Marzano

(2019). Technological help to prevent and reduce cyberbullying. In Cyberbullying and the critical importance of educational resources for prevention and intervention (pp. 119–143). IGI Global. http://doi.org/10.4018/978-1-7998-1684-3.ch012

38.

Mohammed

H. M.

Ali

Q. I.

(2022). Cheating prevention in E-proctoring systems using secure exam browsers: A case study. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 8(4), 634–648. https://doi.org/10.26555/jiteki.v8i4.25094

39.

Murthy

(2024). Social media and youth mental health. U.S. Department of Health and Human Services. https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf

40.

National Center for Education Statistics. (2019). Cyberbullying and cell phone policy in U.S. primary and secondary schools. https://nces.ed.gov/pubs2019/2019053.pdf

41.

Office of the Federal Register, National Archives and Records Administration. (2021, September 30). 47 CFR § 54.520—Children’s Internet Protection Act certifications required from recipients of discounts under the federal universal service support mechanism for schools and libraries. Office of the Federal Register, National Archives and Records Administration. https://www.govinfo.gov/app/details/CFR-2021-title47-vol3/CFR-2021-title47-vol3-sec54-520

42.

Ojeda

Espino

Elipe

del-Rey

(2023). Even if they don’t say it to you, it hurts too: Internalized homonegativity in LGBTQ+ cyberbullying among adolescents. Comunicar, 31(75), 21–35. https://doi.org/10.3916/C75-2023-02

43.

Patchin

J. W.

Hinduja

Meldrum

R. C.

(2023). Digital self-harm and suicidality among adolescents. Child and Adolescent Mental Health, 28(1), 52–59. https://doi.org/10.1111/camh.12574

44.

Patton

M. Q.

(2014). Qualitative research and evaluation methods: Integrating theory and practice. Sage.

45.

Prothero

(2023). Monitoring or blocking what students do online poses all kinds of problems. Education Week . https://www.edweek.org/technology/monitoring-or-blocking-what-students-do-online-poses-all-kinds-of-problems/2023/09

46.

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/

47.

Scheuerman

M. K.

Jiang

J. A.

Fiesler

Brubaker

J. R.

(2021). A framework of severity for harmful content online. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–33.

48.

Securly. (2024). Securly—The student safety company. Securly. https://www.securly.com/

49.

Shapiro

S. S.

Wilk

M. B.

(1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591–611. https://doi.org/10.1093/biomet/52.3-4.591

50.

Sharifi

Siegl

Vance

(2024). Understanding student monitoring [Infographic]. Future of Privacy Forum. https://studentprivacycompass.org/wp-content/uploads/2021/10/FPF_How-Monitoring-Works-v03-1.pdf

51.

Smahel

Machackova

Mascheroni

Dedkova

Staksrud

Ólafsson

Livingstone

Hasebrink

(2020). EU Kids Online 2020: Survey results from 19 countries. Entités externes, EU Kids Online. http://hdl.handle.net/20.500.12162/5299

52.

Smith

P. K.

Livingstone

(2017). Child users of online and mobile technologies – risks, harms and intervention. In Skuse

Bruce

Dowdney

(Eds.), Child psychology and psychiatry (1st ed., pp. 141–148). Wiley. https://doi.org/10.1002/9781119170235.ch17

53.

Stanhope

D. S.

Corn

J. O.

(2014). Acquiring teacher commitment to 1:1 initiatives: The role of the technology facilitator. Journal of Research on Technology in Education, 46(3), 252–276. https://doi.org/10.1080/15391523.2014.888271

54.

Tanweer

Wyss

(2023). Building an acceptable use policy. EdTech Hub. https://doi.org/10.53832/edtechhub.0161

55.

Utter

Polacsek

Emond

J. A.

(2022). Parents’ perceptions of privacy policies and practices for school-issued digital devices: Implications for school practices. Journal of School Health, 92(1), 99–108. https://doi.org/10.1111/josh.13107

56.

Uzorka

Namara

Olaniyan

A. O.

(2023). Modern technology adoption and professional development of lecturers. Education and Information Technologies, 28(11), 14693–14719. https://doi.org/10.1007/s10639-023-11790-w

57.

van Nieuwenhuijzen

Junger

Klein Velderman

Wiefferink

K. H.

Paulussen

T. W. G. M.

Hox

Reijneveld

S. A.

(2009). Clustering of health-compromising behavior and delinquency in adolescents and adults in the Dutch population. Preventive Medicine, 48(6), 572–578. https://doi.org/10.1016/j.ypmed.2009.04.008

58.

Vissenberg

d’Haenens

(2020). Protecting Youths’ wellbeing online: Studying the associations between opportunities, risks, and resilience. Media and Communication, 8(2), 175–184. https://doi.org/10.17645/mac.v8i2.2774