## 5 Unresolved Issues and Future Research  
Special challenges and opportunities arise as regards data collection. Many prevailing predictive modeling projects (e.g., customer relationship management and government profiling) acquire the needed data without the knowledge or consent of the targeted individual--perhaps from web-browsing, credit card, and social media activity, scanner data, facial recognition systems, or immigration registries. The eLearning system described here is quite different in that some level of student involvement with data collection is probably inevitable. Examples of student participation, ranked from least-intrusive to most-intrusive, follow. * _No student involvement_. Many universities have Institutional Review Boards or equivalents that will release legally-protected student information once it has been anonymized. That information might include things like age, ethnicity, prior academic performance, and financial status. That data source might be used for the historical database. * _Minimal student involvement_. Students might grant the release of legally-protected information. This would require, from students, nothing more than consent. * _Moderate student involvement_. Students might provide information about themselves that is not available from existing sources--e.g., whether the student is caring for aging parents or number of hours worked. This might require, from students, the completion of a survey. * _Maximum student involvement_. Students might actively seek out information about themselves that not even they know. For example, students might be asked to determine their personality type or learning styles using publicly-available online assessments such as the International Personality Item Pool ([[http://www.personal.psu.edu/~j5j/IPIP/](http://www.personal.psu.edu/~j5j/IPIP/)]([http://www.personal.psu.edu/~j5j/IPIP/](http://www.personal.psu.edu/~j5j/IPIP/))) or the Felder-Soloman Index of Learning Styles ([[http://www.engr.ncsu.edu/learningstyles/ilsweb.html](http://www.engr.ncsu.edu/learningstyles/ilsweb.html)]([http://www.engr.ncsu.edu/learningstyles/ilsweb.html](http://www.engr.ncsu.edu/learningstyles/ilsweb.html))) [39, 40, 41]. In this scenario, students would complete the questionnaires online, receive the results, and report them to the instructor or database manager. A recent study using predictive analytics in an educational setting found that even rudimentary demographic information about students was sufficient to produce a predictive model with high classification accuracy [25]. That work suggests that extensive student involvement in data collection might not be needed; we will learn more about the requisite level and amount of input information as work in this area unfolds. In this messaging system there is an intricate interplay between data collection and accuracy--the degree to which messages are customized to each individual student. More student records and more information about each student mean richer databases and more accurate predictions; that, though, means student engagement. Student involvement might be encouraged via extra credit in for-credit courses. In some courses, having students learn about their personality types or learning styles might be a natural extension of the topical course coverage (perhaps in education, management, or psychology). There is another possibility for data acquisition: requiring students to supply the needed information. Mandated disclosure of information might violate legal protections such as those covering federally-funded institutions of higher education. But what about MOOCs for which registration and participation in the course is voluntary? Might a condition of course registration include supplying personal information? What about training required as a condition of employment or advancement in a workplace setting? Like most new areas of the law, this unchartered legal territory is evolving.