Ethical and privacy issues in the application of learning analytics

The large-scale production, collection, aggregation, and processing of information from various learning platforms and online environments have led to ethical and privacy concerns regarding potential harm to individuals and society. In the past, these types of concern have impacted on areas as diverse as computer science, legal studies and surveillance studies. Within a European consortium that brings together the EU project LACE, the SURF SIG Learning Analytics, the Apereo Foundation and the EATEL SIG dataTEL, we aim to understand the issues with greater clarity, and to find ways of overcoming the issues and research challenges related to ethical and privacy aspects of learning analytics practice. This interactive workshop aims to raise awareness of major ethics and privacy issues. It will also be used to develop practical solutions to advance the application of learning analytics technologies

Responsible learning analytics: Creating just, ethical, and caring la systems

Ethical considerations and the values embedded in the design, development, deployment, and use of Learning Analytics (LA) systems have received considerable attention in recent years. Ethical frameworks, design guidelines, principles, checklists, and a code of practice have contributed a conceptual basis for focused discussions on ethics in LA. However, relatively little is known about how these different conceptual understandings of ethics work in practice and what specific tensions practitioners (e.g., administrators, developers, researchers, teachers, learners) experience when designing, deploying, or using LA with care.This half-day interactive workshop aims to provide participants with a space for information, dialogue, and collaboration around Responsible LA. The workshop will begin with a brief overview of Responsible LA. After that, the participants will present their cases drawing attention to the ethical considerations covered and not covered in LA practices. Following this, participants in groups will discuss the cases illustrating ethical tensions and create semantic categories to document such edge cases. The collected edge cases will be shared in a wiki or database. The workshop outcomes will help inform LA practitioners on ethical tensions thatneed to be discussed with care while highlighting places where more research work is required.

Responsible learning analytics: creating just, ethical, and caring

Ethical considerations and the values embedded in the design, development, deployment, and use of Learning Analytics (LA) systems have received considerable attention in recent years. Ethical frameworks, design guidelines, principles, checklists, and a code of practice have contributed a conceptual basis for focused discussions on ethics in LA. However, relatively little is known about how these different conceptual understandings of ethics work in practice. This half-day interactive workshop aims to provide participants with a space for information, dialogue, and collaboration around Responsible LA. The workshop will begin with a brief overview of Responsible LA. After that, the participants will present their cases drawing attention to the ethical considerations covered and not covered in LA practices. Following this, participants in groups will discuss the cases illustrating ethical tensions and create semantic categories to document such edge cases. The collected edge cases will be shared in a wiki or database. The workshop outcomes will help inform LA practitioners on ethical tensions that need to be discussed with care while highlighting places where more research work is required

A Framework to Support Interdisciplinary Engagement with Learning Analytics

Learning analytics can provide an excellent opportunity for instructors to get an in-depth understanding of students’ learning experiences in a course. However, certain technological challenges, namely limited availability of learning analytics data because of learning management system restrictions, can make accessing this data seem impossible at some institutions. Furthermore, even in cases where instructors have access to a range of student data, there may not be organized efforts to support students across various courses and university experiences. In the current chapter, the authors discuss the issue of learning analytics access and ways to leverage learning analytics data between instructors, and in some cases administrators, to create interdisciplinary opportunities for comprehensive student support. The authors consider the implications of these interactions for students, instructors, and administrators. Additionally, the authors focus on some of the technological infrastructure issues involved with accessing learning analytics and discuss the opportunities available for faculty and staff to take a multi-pronged approach to addressing overall student success.https://scholarworks.wm.edu/educationbookchapters/1045/thumbnail.jp

The Role of Data Analytics in Education: Possibilities & Limitations

In the last decade, we have seen dramatic increases in the integration of technology within education. It has now become commonplace for K-5 educators to apply learning management systems (LMS) in ways that were previously only seen in higher education contexts. Similarly, on the higher education side, we are seeing a significant increase in online learning evidenced by the growing number of for-profit online colleges and universities (Picciano, 2012). This chapter utilizes Khan’s Learning Framework (Khan, 2001, 2005) to explore the role data analytics can play in education by looking at the possibilities and limitations of analytics

Student Attitudes toward Learning Analytics in Higher Education: "The Fitbit Version of the Learning World"

Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups (N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, “Uninformed and Uncertain,” represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; “Help or Hindrance to Learning,” “More than a Number,” and “Impeding Independence”; represented students' perceptions of the likely impact of learning analytics on their learning. “Driving Inequality” and “Where Will it Stop?” represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how “personal” vs. “collective” purposes or principles can intersect with “uniform” vs. “autonomous” activity. The findings highlight the need the need to engage students in the decision making process about learning analytics

Análisis del aprendizaje: una revisión sistemática de literatura.

The majority of algorithms used in the data analysis are designed according to the capacities of power and flexibility rather than for its simplicity and are too complex to use in the educational context. The objective of this paperis to present a literature review on the learning analytics in higher education: problems, limitations, techniques and tools used. The systematicliterature review methodology was used to answer three research questions on the basis of scientific publications. This paper concludes that it mustimplement, adapt or develop algorithms for the educational context and must be build tools for the analysis of educational data with intuitive interfaces and easy to use.La mayoría de algoritmos utilizados en el análisis de datos están diseñados de acuerdo con las capacidades de potencia y flexibilidad más que por su sencillez, y son demasiado complejos de utilizar en el contexto educativo. El objetivo de este trabajo es presentar una revisión de literatura sobre el análisis del aprendizaje en la educación superior: problemas, limitaciones, técnicas y herramientas empleadas. Se utilizó la metodología de la revisión sistemática de literatura para responder a tres preguntas de investigación tomando como base publicaciones científicas. Se concluye que se deben implementar, adaptar o desarrollar algoritmos predeterminados para el contexto educativo y, también, construir herramientas para el análisis de datos educacionales que cuenten con interfaces intuitivas y fáciles de utilizar

Connecting Learning Analytics and Problem-Based Learning – Potentials and Challenges

Learning analytics (LA) are a young but fast-growing field, which, according to some authors, holds big promises for education. Some claim that LA solutions can help measure and support constructivist classrooms and 21st century skills, thus creating a potential of making an alignment between LA and PBL principles and practices. Despite this argument, LA have not yet gained much interest among the Problem-Based Learning (PBL) practitioners and researchers and the possible connections between PBL and LA have not yet been properly explored. The purpose of this paper is, therefore, to investigate how LA can potentially be used to support and inform PBL practice. We do this by identifying central themes that remain constant across various orchestrations of PBL (collaboration, self-directed learning, and reflection) and present examples of LA tools and concepts that have been developed within LA and neighbouring fields (e.g. CSCL) in connection to those themes. This selection of LA solutions is later used as a basis for discussing wider potentials, challenges and recommendations for making connections between PBL and LA. &nbsp

The Construction and Validation of an Instructor Learning Analytics Implementation Model to Support At-Risk Students

With the widespread use of learning analytics tools, there is a need to explore how these technologies can be used to enhance teaching and learning. Little research has been conducted on what human processes are necessary to facilitate meaningful adoption of learning analytics. The research problem is that there is a lack of evidence-based guidance on how instructors can effectively implement learning analytics to support academically at-risk students with the purpose of improving learning outcomes. The goal was to develop and validate a model to guide instructors in the implementation of learning analytics tools to support academically at-risk students with the purpose of improving learning outcomes. Using design and development research methods, an implementation model was constructed and validated internally. Themes emerged falling into the categories of adoption and caution with six themes falling under adoption including: LA as evidence, reaching out, frequency, early identification/intervention, self-reflection, and align LA with pedagogical intent and three themes falling under the category of caution including: skepticism, fear of overdependence, and question of usefulness. The model should enhance instructors’ use of learning analytics by enabling them to better take advantage of available technologies to support teaching and learning in online and blended learning environments. Researchers can further validate the model by studying its usability (i.e., usefulness, effectiveness, efficiency, and learnability), as well as, how instructors’ use of this model to implement learning analytics in their courses affects retention, persistence, and performance

Understanding Ethical Concerns in the Design, Application, and Documentation of Learning Analytics in Post-secondary Education

University of Minnesota Ph.D. dissertation. August 2015. Major: Rhetoric and Scientific and Technical Communication. Advisor: Ann Hill Duin. 1 computer file (PDF); ix, 138 pages.The practice of predicting a student's level of success in order to provide targeted assistance, termed "learning analytics,"� emerged from a well-established business intelligence model popularly called "Big Data"�. The ethical impact of Big Data on business practices has been undeniable, however, the ethical concerns of Big Data methodology in academia have yet to be explored, as research in this emerging discipline is relatively new. Thus, the overarching question for this study is as follows: How can we use rhetorical, scientific, and technical communication perspectives to understand ethical concerns in the design, application, and documentation of learning analytics in post-secondary education? To investigate this question, I conducted a five-stage study using a cross-disciplinary perspective based on existing frameworks in rhetoric and scientific and technical communication, united by their ethical lens, from genre, persuasion, human-computer interaction, social power, semiotics, visual design, new media literacy, and pedagogy to create a matrix for understanding ethical concerns in learning analytics in post-secondary education. During this study, the inability of students to provide input into the learning analytics process was the concern most often revealed, followed by a lack of context for interpreting the data by both institutional users and students, and the potential inaccuracies in the predictive model caused by inaccurate or incomplete data. Secondary concerns included an undefined institutional responsibility to act on data, which could put the institution at risk for legal action, as well as the possibility for discrimination to occur during the learning analytics process. I provide strategies and responses to address ethical concerns in the design and documentation of learning analytics that should constitute a minimum level of ethical action. This minimal implementation would ensure that students are shown goodwill by the institution (design), and that institutions are properly implementing learning analytics in terms of transparency of process and equality of benefit to the student (documentation). The strategies and responses to address ethical concerns in the application of learning analytics would be more complex for each situation and type of learning analytics used, but should always consider student engagement and success as the priority