Tensions in Student IP

Discerning Tensions in Student-Authored IP Across Platforms

In this section of the webtext, we examine how the types of asymmetry, textual materiality, and circulatory control built into educational technology and social media platforms create tensions in the management of student-authored IP. First, we sketch three hypothetical scenarios that not only demonstrate the complications that students could encounter when the IP they compose is used in unforeseen ways, but also illustrate why it is critically important for students, instructors, faculty, and administrators to consider the IP students might be creating when using these platforms in educational contexts. The scenarios could be used to promote conversations with students about platforms and IP management issues. For instance, students and educators might discuss how IP is introduced into platforms in different ways. In the first scenario, a graduate teaching assistant (GTA) interacts within a learning management system, which sends a student paper to a plagiarism detection service (PDS). Do you think a GTA or an instructor has a right to introduce a piece of student-authored IP to a PDS platform? How could student papers be used by the platform for purposes that go beyond detecting plagiarism? What other questions, issues, or concerns about student-authored IP do you see these hypothetical scenarios raising?

Next, we introduce a heuristic that we developed to help us identify the types of IP tensions that surround composing with platforms. Instructors, faculty, and administrators might apply the heuristic when considering the tensions associated with platforms that are used (or proposed for use) within their classrooms, schools, and universities. The heuristic could also be used to scaffold discussions with undergraduate and graduate students about how the types of IP users contribute to platforms might be utilized by others.

Within each of the subpages of the next section that delves deeper into each of the three platforms—TURNITIN, TWITTER, and CANVAS—we present populated examples of the heuristic introduced below and discuss tensions in authorship that are specific to each platform. While asymmetry, textual materiality, and circulatory control influence how IP is produced, consumed, and subsumed across each of these platforms, our discussion of the respective platforms centers on the ways tensions in authorship manifest within that platform. Specifically, audiences will find that the IP Cast discussions focus on (1) how issues of asymmetry give rise to tensions in the management of student-authored IP in the plagiarism detection service Turnitin, (2) how textual materiality gives rise to tensions in the management of student-authored IP in the social media platform Twitter, and (3) how circulatory control gives rise to tensions in the management of student authored IP in the learning management system Canvas.

Student Authorship Matters
Content Warning/Trigger Warning: The following scenarios illuminate worst-case consequences associated with student IP flowing through platforms to tertiary audiences outside of universities and schools and involve descriptions of interpersonal violence and law enforcement that readers may find triggering.

Privacy and Plagiarism

A land-grant university purchases access to embed plagiarism-detection software (PDS) within a university learning management system (LMS). The syllabus notes that all papers submitted for class will also be submitted to the PDS in an effort to deter plagiarism. An undocumented student at the university without Deferred Action for Childhood Arrivals (DACA) status has enrolled in a first-year writing class. They submit a paper about immigration policy through the LMS. The instructor of the course—a graduate teaching assistant who has been taught that PDS is a valuable tool for ensuring academic integrity—begins evaluating submissions. They click a button in the LMS that sends the paper, including personally identifying information (PII) about the student, to the PDS. The PDS crawls the paper and compares the submission to a database of nearly one million student papers that has been amassed over a 20-year period. The papers are stored in a PDS database that includes the names of students. Recently, law enforcement officers from Immigration and Customs Enforcement (ICE) have been attempting to identify undocumented students enrolled at a land-grant university located in a sanctuary city. After consulting university lawyers, administrators at the university declined to give student records to the law enforcement agency. Rather than get involved in a protracted legal battle, ICE shows up at the headquarters of the PDS with a national security letter demanding that the platform provide it access to the company's databases, so it can identify undocumented students recently enrolled at the university.

Harm and Social Media

A first-year writing instructor decides that they will incorporate Twitter in the classroom as a way of getting students to engage with class content, while also learning an awareness of participating in the public sphere. They set aside one class to help students create accounts and learn the platform. They establish a course hashtag and discuss the plan to have students tweet responses to the readings, regularly responding to one another as an additional facet of class participation. Participation is, of course, required per the syllabus. Several weeks into the course, the class reads articles about rhetorics of oppression. One student, a woman of color, tweets about how the readings speak to her own experiences. She includes hashtags such as #BlackLivesMatter, #MMIW, and #DistractinglySexy in her reading response tweets and comments to other women in class about race and gender. By the next morning, she sees that her tweets have been retweeted many times, including by those hostile to her point of view who have invited others to respond. Her notifications are a mess of responses. There are death threats by accounts with no avatars. Some users have found her home address and tell her they are going to rape her. Others have stalked her and posted a picture of her walking to class. While this student has fulfilled the assignment asking her to create public-facing content as a class requirement, she is currently experiencing an onslaught of online harassment and violence that goes beyond the classroom.

Data Mining and Learning Management

A tenured professor who teaches a computer science class regularly uses the learning management system (LMS) to support activities and learning in the course. They upload PDFs of course readings, use the assignment tool to receive submissions from students, respond to student writing using comment tools that are embedded within the LMS, host out-of-class discussion on the discussion board, and post grades in the system's grade-book. Last semester, the professor attended a workshop where a colleague raved about how using an additional tool in the LMS increased student engagement and participation in the course. This semester the professor decided to try the tool out by clicking an option in the LMS that enabled the application. Later that semester, the professor is caught off-guard when a student attends office hours—irate. The student explains that they have read a blog post about a discussion application called Piazza that the professor required students to use in the course. The student is understandably upset to have learned that the app has not only been mining data from their participation in class discussions but also that the app sells that data to corporations. The professor is baffled, as they had believed that the Family and Educational Rights and Privacy Act (FERPA) prevented the university from sharing information about students with third parties.

Discerning Tensions in Authorship

A Heuristic for Analyzing Platform Tensions in IP Exchanges

Here we offer a heuristic, Table 1. A Heuristic for Analyzing Platform Tensions in Student IP Exchanges, that we've developed to help students, educators, administrators, platform designers, and/or researchers identify how IP might be generated, circulated, and recomposed within technology platforms used in educational settings. This heuristic might be used for reflection, analysis, and research, but is also intended as a tool for decision-making. Individuals or groups can use the heuristic to better understand the characteristics of a particular educational technology platform and particular uses or applications of an educational technology platform, both of which have implications for student intellectual property. As our discussion elsewhere in this webtext demonstrates, the implications of particular uses of educational technology platforms for student intellectual property can be nuanced, non-obvious, and complex. By applying the heuristic, we hope that students, researchers, instructors, and administrators can parse out some of the more nuanced but important characteristics of platforms and/or bring to light aspects of composing and interacting with platforms that are not immediately apparent.

The heuristic offers six categories for considering the interrelationships of IP and educational technology platforms:

Because each of these categories is complex, our heuristic provides an attributes column that provides aspects of the category that can be used to help guide a platform analysis. By describing these categories of an educational technology platform with this level of detail, analysts might glean richer views of the ways asymmetry, textual materiality, and circulatory control manifest within platforms and how those issues carry implications for student authors and the IP they compose. Simply put, the heuristic is intended as a starting point for discussion, analysis, and decision-making.

Table 1. A Heuristic for Analyzing Platform Tensions in Student IP Exchanges
Category Category Attributes
Users are individuals who are integrated into platforms within specific roles. These roles influence the types of content, data, or metadata they might introduce to a platform. Oftentimes, users who occupy different roles will have differential access to the content, data, or metadata that exists within a platform or they might interact with content, data, or metadata in distinct ways. Institutional: students, instructors, administrators, program directors, IT staff, academic staff

Platform: developers, employees, third parties, advertisers

Public: publics and counterpublics, supporters and adversaries, trolls, predatory actors

Technology: bots, plugins, cookies, web beacons
Permissions are the processes, default settings, policies, and licensing agreements through which users access platforms and/or give consent for platforms to make use of the content, data, and metadata they contribute. Policies: terms of use policies, copyright policies, end user licensing agreements

Laws: copyright laws (e.g., DMCA; TEACH), privacy laws (e.g., FERPA)

Settings: default settings, options to change settings, processes available to users to manage settings

Processes: subscription models; click-wrap agreements; informed consent; institutional, programmatic, or instructional coercion
Inputs are the contributions of content, data, and metadata that users provide, make, share, upload, or compose by using or interacting with a platform. Content: student papers, readings, teaching materials, blog posts, tweets, status updates, photos, audio files, videos, messages, and other communications

Data: student names, student grades, profile information and other student-inputted information, hashtags, retweets, shares

Metadata: time logged in, pages visited, file types and file metadata, IP address, location, geolocation, browser and hardware used
Operations are the interactions—whether human or computational—that the platform/service technology, users, or service/platform providers perform with or upon inputs. Human: shares, retweets, comments, ratings, messaging, liking, following, curating followers

Computational: spiders or indexing tools; content and data collection and storage; tracking user clicks, pages visited, and other activities within across platforms; sharing of inputs or outputs with third parties; use of inputs and outputs by third parties
Outputs are derivative products which make use of inputs that arise as a result of platform use, user interactions, or operations performed upon inputs within a platform. Examples: reports, marketing insights, trend assessments, databases, user profiles
Gateways are pathways by which third parties beyond the composing author (Party 1) and platform/service providers (Party 2) might gain access to the (Inputs) content, data, or metadata or (Outputs) products, value, content, or data. Institutional: students, instructors, and administrators are granted distinct user roles within a platform, which provides differential access to specific types of Inputs, Operations, and/or Outputs within a platform.

Surface: access by students, developers, employees, third parties, or advertisers to Inputs, Operations, or Outputs that are either publicly available or available to users through surface-level interactions within a platform.

Server-side: access by users, developers, platform employees, third parties, or advertisers to Inputs, Operations, or Outputs gained through commercial, proprietary, or computational mechanisms. A marketing firm might pay a platform for information about users; a platform might construct robust databases on what it knows about users; a researcher might write a program that communicates with a platform's application programming interface (API) that allows them to pull information down from a database.

Technological: exploiting vulnerabilities in a platform or making use of other computational tools to access a platform's Inputs, Operations, or Outputs in ways that are not illegal, unsanctioned by, or unknown to a platform.

IP Cast 3. Heuristic Walkthrough. Tim describes the categories within the heuristic, discussing how students, teachers, or researchers consider these categories when applying the heuristic to a platform. Broadly, we envision the heuristic being a tool to take stock of six platform elements that influence the ownership or authorship of IP created within or passing through platforms. Once an analyst has populated the "category attributes" column they might consider how the (in)visibility of user contributions, asymmetry in users' access to and control over various contributions, and potentials for circulation and recomposition of contributions might create tensions in IP management. A transcript of the conversation has also been provided.

For example, in the hypothetical scenario above titled "Privacy and Plagiarism," a student (user) had logged into (Operation) a learning management system (LMS) using a university provided username and their unique password (Gateway) to submit a writing assignment (Input). To get a better sense of permissions that surround this scenario, an analyst could research terms of use governing IP and student privacy in the LMS, identify university policies that govern student IP and privacy, and determine whether the student had actually read and understood these policies when they clicked the "I agree" box to use the LMS. Student analysts familiar with the LMS might know that the student interface displays an "assignment submitted" message (Output). Instructor analysts familiar with the LMS might know that the instructor (User) interface provides additional information about the submission such as time of submission (Operations and Inputs) and the instructor (but not the student) can access additional data (outputs) about many of the course readings each student has clicked on, downloaded, and the amount of time each student has spent on those readings (Inputs and Outputs). In the scenario, the instructor is also given the ability to press a button which submits the student's paper to a plagiarism detection tool that has been directly embedded within the learning management system (Gateway). Later, we learned that students are not made aware when/if their papers have been submitted (Operation performed on student input not made apparent to student user).

In this hypothetical example, the instructor took student IP and introduced it into a third platform without the student's direct knowledge. Issues of asymmetry (differential roles within the platform and power within a classroom), textual materiality (data and metadata that accompany student interactions within a platform or contextualize a student's submission), and circulatory control (an instructor has a choice to introduce a piece of student IP into a PDS, a third party beyond the LMS) are all at play and have implications for this student and their IP.

In the subpages of the Platforms section, Turnitin, Twitter, or Canvas, we have applied the heuristic to three platforms widely used in educational settings. Here audiences will find populated examples of the heuristic. We hope that these examples help to illuminate the obscured, hidden, or complex attributes of these platforms and scaffold deeper understanding of the IP implications associated with student authorship and platform-based composing. In the analysis of Turnitin, for instance, we note that while the originality report is the obvious product of use, another output of the platform is a proprietary database of student writing. We believe that recognizing the value of student-authored IP—attending to the derivative products platforms make by repackaging student (and faculty) inputs—might lead institutional decision-makers to reconsider whether requiring students to submit their IP to platforms is in the long-term interests of schools and universities. Similarly, by applying the heuristic to Twitter, we can see the ways in which the platform encourages circulation of content (visible) and data (tacit): to name a few, default permissions set access as public, inputs include hashtags and invite other forms of operations such as retweets and replies that allow easy distribution, as well as gateways such as Twitter's application program interface (API) that provide access to Twitter data to identify trends and act on them (outputs). Understanding the multiple and multifaceted affordances that allow Twitter to function as a social network could affect how and whether we ask students to compose using the platform. Finally, an analysis of Canvas using the heuristic highlights the many users – within the educational institution and beyond—and gateways that provide access to student content and data, much of which is problematically viewed and collected without students' knowledge.

By viewing each educational technology platform through the lens of these six categories, it is our hope that instructors, administrators, and other decision-makers can make better-informed choices about their adoption and application of platforms within educational contexts. Researchers interested in better understanding the implications of educational technology platforms for IP can also apply it in their analyses. Additionally, we believe the heuristic can be a useful tool for students, who are asked to use the technologies for writing and learning, and who may have the most at stake. Engaging students in analyzing educational technology platforms and their implications for their own authoring and composing processes serves as an important learning experience for them, with the potential for including them in the decision-making process of selecting educat

IP Cast 4: Uses for the Heuristic

IP Cast 4. Uses for the Heuristic. Jessica, Les, and Tim provide examples of the purposes they envision students, educators, administrators, and researchers using the heuristic to realize. A transcript of the conversation has also been provided.



The information and ideas contained in this webtext are not intended to be understood as legal advice, but rather as an exploration of the potential tensions that may exist between how authorship functions as a legal concept and how authorship is practiced and theorized in educational contexts.

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