Online and Traditional Lectures: Evaluating Effects of Social Presence and Learner Control

Online courses are rapidly replacing traditional, face-to-face lectures in American universities (Allen & Seaman, 2011). As technology improves, this trend will likely continue and accelerate. Researchers must evaluate the impact of online courses compared to their traditional counterparts. This two-part study quantifies the effect of two variables – social presence and learner control – on students’ recall, application and perceived learning levels in different lecture formats. Students in introductory courses at a four-year, public, American university were randomly assigned into three groups to view distinct lecture formats, one in a traditional classroom and two via the Internet. Upon viewing the single lecture, the students were asked to fill out a test and survey to quantify teacher immediacy, recall and application, and perceived learning levels across lecture formats. The study found that different levels of social presence and learner control affected students’ perceived learning levels but did not impact recall or application.

Adapting Behavioral Parent Training as an Interactive Computer Game

Behavioral Parent Training (BPT) is a well-established therapy that reduces child externalized behaviors and parent stress. Although BPT was originally developed for parents of children with defiant behaviors, the program’s key concepts are relevant to parenting all children. Since parents might not fully utilize BPT due to cost and program location, we created an online game as a low-cost, easily accessible alternative or complement to BPT. We tested the game with nineteen undergraduate students at the University of Maryland. The experimental group completed pretest survey on core BPT knowledge, played the game, and completed a BPT posttest, while the control group completed a pretest and posttest survey over a three week period. Participants in the experimental group also completed a survey to indicate their satisfaction with the overall program. The experimental group demonstrated significantly higher levels of BPT knowledge than the control group and high levels of satisfaction. This suggests that an interactive, online BPT platform is an engaging and accessible way for parents to learn key concepts.

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.