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

PassNote: A Feedback Tool for Improving Student Success Outcomes

When Purdue University faculty asked for assistance in composing feedback messages to students, Information Technology at Purdue (ITaP) developed PassNote, a feedback tool that integrates good practice into the process of providing formative assessments. PassNote gives faculty customizable feedback prompts (snippets) and lets them connect students with information and links to services such as tutoring,Supplemental Instruction, library resources, technology tools, and workshops. PassNote message starters are often incomplete, allowing instructors to include course-specific information such as office hours and departmental resources

Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework

In this article, we argue that the contributions of documentation studies can provide a useful framework for analyzing the datafication of students due to emerging learning analytics (LA) practices. Specifically, the concepts of individuals being ‘made into’ data and how that data is ‘considered as’ can help to frame vital questions concerning the use of student data in LA. More specifically, approaches informed by documentation studies will enable researchers to address the sociotechnical processes underlying how students are constructed into data, and ways data about students are considered and understood. We draw on these concepts to identify and describe three areas for future research in LA. With the description of each area, we provide a brief analysis of current practices in American higher education, highlighting how documentation studies enables deeper analytical digging

D3.1 Framework of Quality Indicators

D3.1 Framework of Quality Indicators. LACE Projec

Advising the whole student: eAdvising analytics and the contextual suppression of advisor values

Institutions are applying methods and practices from data analytics under the umbrella term of “learning analytics” to inform instruction, library practices, and institutional research, among other things. This study reports findings from interviews with professional advisors at a public higher education institution. It reports their perspective on their institution’s recent adoption of eAdvising technologies with prescriptive and predictive advising affordances. The findings detail why advisors rejected the tools due to usability concerns, moral discomfort, and a belief that using predictive measures violated a professional ethical principle to develop a comprehensive understanding of their advisees. The discussion of these findings contributes to an emerging branch of educational data mining and learning analytics research focused on social and ethical implications. Specifically, it highlights the consequential effects on higher education professional communities (or “micro contexts”) due to the ascendancy of learning analytics and data-driven ideologies

The Syllabus as a Student Privacy Document in an Age of Learning Analytics

Purpose The purpose of this paper is to reveal how instructors discuss student data and information privacy in their syllabi. Design/methodology/approach The authors collected a mixture of publicly accessible and privately disclosed syllabi from 8,302 library and information science (LIS) courses to extract privacy language. Using privacy concepts from the literature and emergent themes, the authors analyzed the corpus. Findings Most syllabi did not mention privacy (98 percent). Privacy tended to be mentioned in the context of digital tools, course communication, policies and assignments. Research limitations/implications The transferability of the findings is limited because they address only one field and professional discipline, LIS, and address syllabi for only online and hybrid courses. Practical implications The findings suggest a need for professional development for instructors related to student data privacy. The discussion provides recommendations for creating educational experiences that support syllabi development and constructive norming opportunities. Social implications Instructors may be making assumptions about the degree of privacy literacy among their students or not value student privacy. Each raises significant concerns if privacy is instrumental to intellectual freedom and processes critical to the educational experience. Originality/value In an age of educational data mining and analytics, this is one of the first studies to consider if and how instructors are addressing student data privacy in their courses, and the study initiates an important conversation for reflecting on privacy values and practices

A Student Advising System Using Association Rule Mining

Academic advising is a time-consuming activity that takes a considerable effort in guiding students to improve student performance. Traditional advising systems depend greatly on the effort of the advisor to find the best selection of courses to improve student performance in the next semester. There is a need to know the associations and patterns among course registration. Finding associations among courses can guide and direct students in selecting the appropriate courses that leads to performance improvement. In this paper, the authors propose to use association rule mining to help both students and advisors in selecting and prioritizing courses. Association rules find dependences among courses that help students in selecting courses based on their performance in previous courses. The association rule mining is conducted on thousands of student records to find associations between courses that have been registered by students in many previous semesters. The system has successfully generated a list of association rules that guide a particular student to select courses. The system was validated on the registration of 100 students, and the precision and recall showed acceptable prediction of courses

Connectivisim theory to develop english listening skills on senior students at Monte Olivo High School Academic period 2021 -2022

Analyze the Connectivism theory for the development of English listening skills on senior students at Monte Olivo High School.Learning theories understand how people acquire different types of knowledge and describe methods to make understanding clear. Assuming that in this research work are described five learning theories that were used to reach the main objectives. They are Behaviorism learning theory; it is based on the stimulus-response scheme and is based on objective and experimental procedures. It determines that learning is based on human behavior change and modifies behavior through stimulation, response, and reinforcement. This research is supported by the Connectivism learning theory, it is one of the most important theories in the present research because it uses technology as an important role in society. Furthermore, their technological tools helped create the proposal's creation activities, which is useful for students in their education in the digital age. In the globalized world acquiring a second language is important because it allows people to relate to different labor branches. Language acquisition involves structures, rules, and representation. The capacity to use language successfully requires one to acquire a range of tools including phonology, morphology, syntax, semantics, and an extensive vocabulary. This research is focused on listening skills, language is one of the essential parts, because it allows students not only to understand but also to imitate sounds and to be understood more clearly, in addition to being able to understand the speakers of the language. It refers to the capacity for listening comprehension and, as we mentioned at the beginning, it allows us to imitate sounds and improve precision when speaking. The reason for the importance of Listening is that when people are babies and children, they only identify the phonemes of their own language and do not consider the others, because it does not resemble what they were used to since they were born.Licenciatur

A matter of trust: : Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution’s responsibility to its students