Kyle M. L. Jones

Community of Practice or Affinity Space: A Case Study of a Professional Development MOOC.

Massive Open Online Courses (MOOCs) have brought about new questions regarding the construction of virtual learning environments and course delivery systems. One such question that researchers and instructors alike are considering is the role of community in learning spaces. This paper uses a professional development (PD) MOOC as a case study to investigate whether students identified as a community and identify characteristics that encourage students to believe this is so. Furthermore, the authors question if “community,” specifically “communities of practice,” is actually the appropriate way to describe such learning spaces, or if the concept of affinity spaces is more useful. The findings reveal important implications for designing course spaces for community experiences and the conceptual approach to professional development communities in large-scale courses.

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.

Learning Analytics and Its Paternalistic Influences

Learning analytics is a technology that employs paternalistic nudging techniques and predictive measures. These techniques can limit student autonomy, may run counter to student interests and preferences, and do not always distribute benefits back to students–in fact some harms may actually accrue. The paper presents three cases of paternalism in learning analytics technologies, arguing that paternalism is an especially problematic concern for higher education institutions who espouse liberal education values. Three general recommendations are provided that work to promote student autonomy and choice making as a way to protect against risks to student academic freedom.