By the year 2018, 65% of all jobs available to Hawaii students will require some postsecondary training or education1. More University of Hawai‘i System students were enrolled in remedial or developmental math courses (36%) than college-level math courses (24%) in 2012. The Online Learning Academy at the University of Hawai‘i (UH) is striving to prepare students in math for successful futures in either postsecondary training programs or higher education.

The OLA provides free online math tutoring to all Hawaii Department of Education students state-wide. Live, online tutoring with a university student provides a math support solution, especially for schools in rural and/or remote areas. In addition to online tutoring, the OLA offers free math prep programs, using the online platform called EdReady, for commonly used college math placement exams (e.g., ACT, SAT and COMPASS).

This session will present how online technology is a viable solution for students seeking additional academic support in math. Best practices for e-learning, college readiness preparation and online mentoring will be discussed as part of the presentation. The presentation will also discuss future plans for implementing EdReady and online tutoring to a wider audience.

The purpose of TechLogic is to provide a platform for thoughtful, logical, and collaborative exchange about teaching and learning with technology at the University of Hawai‘i. TechLogic@UH provides an annual system-based, community-driven platform for developing a community of practice grounded in context and experience. This session will present the rationale and theoretical construct for developing a community of practice around educational technology at UH Manoa, “tech story” about the development and institutional challenges, and solicit feedback for continued growth aligned with the goal and purpose.

What is modularization, and why has this become a buzz word in online education in recent years? This general session seeks to explore online course modularization, and the changing concept of “going to class,” particularly in humanities courses. As classes both on ground and online are unbundled, this reflects changing technology applications both inside and outside higher education; applying this unbundled approach in online humanities courses addresses challenges online students may experience with course content consistency, as well as the digital divide; creating comprehensible course content and unlimited access to information cultivates engagement in online undergraduate courses, and empowers students to continue pursuing college courses. As a point of emphasis, the presenters will use case studies from one course at Kaplan University, “Humanities and Culture”. As part of this presentation, the presenters seek to encourage conversation regarding an understanding of what the term “modularization” entails in online education, as well as how modular design, with its components of pre-and-post-assessment activities, clear learning objectives, and assigned reading and writing activities, empowers students, particularly in humanities courses.

Profiling elearning students is becoming a common practice in the field (e.g., Yukselturk & Top, 2013). Using a Web survey or questionnaire, an increasing number of learner characteristics and demographics can be studied in a form of data. Given this easy access to the collected data, researchers have attempted to take into account multiple (i.e., two- or more-way) profiling variables (e.g., gender and grade level) at once, in lieu of dealing with one variable at a time. This attempt makes the design of their study more sophisticated and more versatile. It also assists the researchers in finding hidden patterns of the learners and their behaviors (Shih, Jheng, & Lai, 2010). Most importantly, their study results enable the top management team to make informed decisions. One major advantage of two-step cluster analysis is it allows researchers to consider both continuous/numerical and categorical/nominal variables at a time as other clustering techniques, such as K-Means Cluster and Hierarchical Cluster in SPSS, are limited respectively, as Şchiopu (2010) pointed out.

In this phase of the investigation, we plan to (a) follow up on the recommendation for further research we stated in an earlier study on learner preference in the type of the elearning course and their use of social networking tools and (b) explore plausible patterns of learner characteristics/behaviors in relation to their use of social networking tools and choices over the elearning course types. That is, the secondary or archival data with a sample size of approximately 2,000 (undergraduate students of a U.S. southern state university) will be analyzed for the present quantitative study. The data were initially collected in a joint effort of the participating university and EDCAUSE Center for Applied Research (ECAR) in 2013.
This short general session is intended to benefit university distance education management team members and other related policy makers.