学習管理システム「Canvas」のデータ収集と倫理

The Canvas Learning Management System (LMS) is used in thousands of universities across the United States and internationally, with a strong and growing presence in K-12 and higher education markets. Analyzing the development of the Canvas LMS, we examine 1) ‘frictionless’ data transitions that bridge K12, higher education, and workforce data 2) integration of third party applications and interoperability or data-sharing across platforms 3) privacy and security vulnerabilities, and 4) predictive analytics and dataveillance. We conclude that institutions of higher education are currently ill-equipped to protect students and faculty required to use the Canvas Instructure LMS from data harvesting or exploitation. We challenge inevitability narratives and call for greater public awareness concerning the use of predictive analytics, impacts of algorithmic bias, need for algorithmic transparency, and enactment of ethical and legal protections for users who are required to use such software platforms. Discover the world's research25+ million members160+ million publication pages2.3+ billion citationsJoin for free

The case of Canvas: Longitudinal datafication throughlearning management systemsRoxana Marachiaand Lawrence QuillbaDepartment of Teacher Education, San José State University, San José, CA, USA;bDepartment of PoliticalScience, San José State University, San José, CA, USAABSTRACTThe Canvas Learning Management System (LMS) is used in thousandsof universities across the United States and internationally, with astrong and growing presence in K-12 and higher education markets.Analyzing the development of the Canvas LMS, we examine 1)‘frictionless’data transitions that bridge K12, higher education, andworkforce data 2) integration of third party applications andinteroperability or data-sharing across platforms 3) privacy andsecurity vulnerabilities, and 4) predictive analytics and dataveillance.We conclude that institutions of higher education are currently ill-equipped to protect students and faculty required to use theCanvas Instructure LMS from data harvesting or exploitation. Wechallenge inevitability narratives and call for greater publicawareness concerning the use of predictive analytics, impacts ofalgorithmic bias, need for algorithmic transparency, and enactmentof ethical and legal protections for users who are required to usesuch software platforms.ARTICLE HISTORYReceived 31 July 2019Accepted 4 March 2020KEYWORDSData ethics; data privacy;predictive analytics; highereducation; dataveillanceIntroductionThe Canvas Learning Management System (LMS) is used in thousands of universities acrossthe United States and internationally, with a strong and growing presence in the K-12 andhigher education markets. Massive amounts of data are gathered continuously on studentsand faculty often without their awareness or consent. On a regular basis, changes are madeto platform design features and analytics without formal announcement or notification.This paper examines the datafication of higher education through the Canvas LMS.While there is growing interest in educational data-mining in K-12 environments (seeManolev, Sullivan, and Slee 2018) relatively little has been written about the growingmarkets for parallel and connected technologies across and beyond early learning, K12,and higher education sectors. With markets currently steeped in artificial intelligenceand attempts at predictive modeling, longitudinal datasets are an increasingly valuablecommodity and ripe for extraction by private companies.Analyzing the development of the Canvas LMS we examine 1) ‘frictionless’data tran-sitions that bridge pre-K, K12, higher education, and workforce data 2) the integration ofconnected third party applications within the LMS, and interoperability or data-sharing© 2020 Informa UK Limited, trading as Taylor & Francis GroupCONTACT Roxana Marachi [email protected] Department of Teacher Education, San José State University,San José, CA, USAThis article has been republished with minor changes. These changes do not impact the academic content of the article.TEACHING IN HIGHER EDUCATION2020, VOL. 25, NO. 4, 418–434https://doi.org/10.1080/13562517.2020.1739641

across these applications 3) privacy and security vulnerabilities, and 4) predictive analyticsand dataveillance.We conclude that institutions of higher education are currently ill-equipped to protectstudents and faculty required to use the Canvas Instructure LMS from data harvesting orexploitation. We challenge inevitability narratives for blind adoptions of such systemsand call for greater public awareness among members of college and university communitiesconcerning the use of predictive behavioral and learning analytics, the impact of algorithmicbias, the need for algorithmic transparency, and enactment of both ethical and legal protec-tions for users who are required to use such software platforms in educational settings.We situate our own discussion between the ongoing debate within the educational tech-nology sector as it attempts to apply its business models to public higher education, theeconomic advantages afforded to (often cash-strapped) public institutions of higher edu-cation, which seek to promote online courses and degrees while simultaneously encouragingfaculty to switch to the new platform, and those academic sectors that embrace educationaltechnology and seek to promote its use often for ideological purposes (Apple 2004,2005,2007; Boggs and Van Baalen-Wood 2018; Fathema, Shannon, and Ross 2015). We alsorefer frequently to San José State University, our home institution, as a prime example ofa university that finds itself caught between the competing and often conflicting demandsof legislators, educational administrators, faculty, parents, and students.A brief history of CanvasThe rise of the Internet has fundamentally altered teaching and learning. The possibility ofproviding online content directly to students of higher education has opened up vastmarkets for professional certificates, executive education, and degrees beyond the kindsthat have historically been granted at colleges and universities. Proponents of EducationalData Management (EDM) claim that it enables real-time assessment of students andmaterials, and allows institutions to assess the effectiveness of learning strategies, which,in some instances might extend to physically tracking students (through the use ofRFID tags in student IDs) and analysing social networks, thereby adapting content tosupport individuated learning. Using these data to assist in instruction, advising, andresource allocation within institutions is now commonplace (Rubel and Jones 2016).Initially adopted by the Utah Education Network, Canvas by Instructure is now themost widely used LMS provider in the United States and Canada and is only third toGoogle and Microsoft in the amount of student data amassed (Menard 2019). It cancount not just K-12 schools, colleges and universities among its clients but also high-profile corporations like Cisco, which teamed up with Canvas in 2012 for integrationinto its Cisco Networking Academy with over one million students worldwide. In fact,Canvas’reach is truly global with an increasing focus since 2013 upon new educationalmarkets in Central and South America, Africa, Europe, and Asia (see Hill 2017).Started in Utah by two graduate students who designed a Learning ManagementSystem as a class project, Canvas’approach was significantly different from that under-taken by early advocates of educational software. Instead of designing a completesystem for a single institution and adapting the technology for subsequent clients, theopen source approach developed by Canvas offered a suite of services according to needalong with a number of third-party applications offered free of charge.TEACHING IN HIGHER EDUCATION 419

※ 著作権に配慮し、引用は冒頭3段落までです。続きは元記事をご覧ください。