Printing: This poster is 48” wide by 36” high. It’s designed to be printed on a large-format printer.

Customizing the Content: The placeholders in this poster are formatted for you. Type in the placeholders to add text, or click an icon to add a table, chart, SmartArt graphic, picture or multimedia file. To add or remove bullet points from text, just click the Bullets button on the Home tab. If you need more placeholders for titles, content or body text, just make a copy of what you need and drag it into place. PowerPoint’s Smart Guides will help you align it with everything else. Want to use your own pictures instead of ours? No problem! Just right-click a picture and choose Change Picture. Maintain the proportion of pictures as you resize by dragging a corner.

SPEQ-DB: Simple Program for Enhancing Quality in Discussion Boards

Sara Colon Cerezo***, Nicole Dubin*, Rafael Hernandez**, Dr. Brian Thoms* *California State University, Channel Islands, **Oxnard College, ***Santa Barbara City College

PROJECT BACKGROUND Under the field of human computer interaction, the subfield of captology guides how technology can influence behavior [1]. Through an analysis of previous online conversations, this research improves upon the design of an existing online discussion board to incorporate a simple algorithm to facilitate on-topic and readable discussion posts. In total, 1,629 conversations were mined for readability and keyword density. Readability was accessed using Readability Metrics, an open-source application programming interface for managing texts and their readability scores [2]. Key-word density was calculated as a ratio of total keywords found over total words posted minus all stop-words. Our analysis found that while readability increased from originations to responses, there was a 10% decrease in response readability (Figure1) and a 13% decrease in response keyword density (Figure2). Additionally, there was the tendency for users to move away from topics as discussions aged, which is evident by the downward slope of Figure2.

SYSTEM ARCHITECTURE

USER INTERFACE DESIGN

SYSTEM CODE

RESEARCH METHODOLOGY

[1] Fogg, B., & Nass, C. (1997). “Silicon sycophants: The effects of computers that flatter,” Int’l Journal of Human Computer Studies, 46(5). [2] Ipeirotis, P. (2012). “Readability Metrics API,” Mashape. Accessed June 19, 2016 from https://market.mashape.com/ipeirotis/readability-metrics. [3] Hansen, D., Shneiderman, B. and Smith, M. (2011). Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Burlington: Morgan Kaufmann. [4] Simon, H. (1996). The Sciences of the Artificial Third Edition, Cambridge, MA : MIT Press. [5] Thoms, B., Eryilmaz, E. (2014). “How Media Choice Affects Learner Interactions in Distance Learning Classes,” Computers & Education, v75, pp.112-126

ACKNOWLEDGMENTS •  Funding provided by the Title V, US Department of Education Grant •  Project ACCESO Summer Research Institute

REFERENCES

In design science research, researchers are concerned with the way things ought to be in order to attain goals and they construct artifacts as a way of achieving these goals [4]. Building atop [5], this research asks the following research questions: q  R1: To what extent will S.P.E.Q. DB enhance the quality of both origination

and response posts in online conversations? q  R2: To what extent will S.P.E.Q. DB increase levels of network density

within the online community?

Figure 4 represents S.P.E.Q. DB. The primary goals of the new design are to influence higher quality interactions and facilitate a more cohesive social network by providing a responsive way for users to judge the quality of their posts. Users can view the quality report by clicking the Analyze Button below the textbox. Design improvements include: Ø  Group QI: Calculated using the average for all individual posts for that

discussion. Ø  Individual QI: Calculated using the new QI formula. Ø  Quality Gauge: Compares the individual QI against the group’s average QI. Ø  User Pins: Allows users to keep track of posts they are interested in.

SNA graphs were constructed in NodeXL, which is an open source extension for MS Excel that provides a range of basic network analytics and visualization features [3]. Each node represents a user, and each edge (i.e. line between two nodes) is an interaction between users (i.e. responding to a post). Summary: Most users who fall within an acceptable level of quality tend to be more central to the network, while users with lower quality scores tend to be situated on the outskirts of the social network. This finding suggests that as the quality of user posts increases, the number of responses that user receives also increases, thus increasing the density of the social network.

q  Blue Disks represent users whose QI falls more than one standard deviation above the mean QI.

q  Green Squares represent users whose QI falls within one standard deviation of the mean QI.

q  Red Triangles represent users whose QI falls more than one standard deviation below the mean QI.

q  Node sizes represent the sum of responses a user received per post.

q  Labels indicate the NodeID and QI.

Identifying 1) topic focus and 2) readability as important factors that influence the flow of online conversations, a simple formula for determining the quality index (QI) was constructed resulting in even higher disparities between originations and responses (Figure3).

Figure 4. User Interface

S.P.E.Q. Discussion Board

Step 1: Calculates readability and key word density for users posts.

Step 3: Calculates post level QI.

Step 2: Calculates thread level readability and key word density values.