Social Media and Big Data for Better MIS and GIS Teaching and

Learning

by

Mehrdad Koohikamali

James Pick

School of Business

University of Redlands

Agenda

• Main Focus

• Motivations

• Research Questions

• Methodology and Data Analysis

• Results

• Findings and Implications

• Conclusion and Future Plan

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Motivations

• Increased but yet marginal usage of technology in classrooms

• Minimal usage of social media contents in teaching and learning (Griesemer, 2012)• Instructors and Students, both benefit

• Increase in availability of geo-tagged contents on social media (Menfors and Fernstedt, 2014)

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Social Media and Big Data

• Social media promises to accelerate students’ engagement, innovation, and learning capacities (Ali et al., 2016)

• Mutual use of social media and big data analytics improves teaching and learning experiences

• Social media meets the definition of big data, since it has large volume, high velocity and great variety (diverse forms of unstructured content) (Davenport, 2014).

• Information on social media provides a timely and cost-effective resource (Hausmann et al., 2017)

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Social Media Use in Classes:Three-step Approach

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Content Extraction

Quality Evaluation

Geo-Visualization

Research Questions

• How can social media big data reshape the way students find/seek/receive course related materials such as case studies, topic discussions, and feedbacks?

• What are the metrics for evaluating the quality of social media big data contents?

• How visualizng geo-tagged contents on social media help a student’s discovery on a variety of business and technology topics?

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Methodology

• Context of study• BUAD683, Information and Knowledge Management (B.S. Bus. Course)

• INFT 625, Special Topics in Information Technology (MSIT course)• Sample size: 37 Students, 9 Groups, 3 Campus Locations

• Twitter used as the social media platform due to data availability

• Social Media Extraction and Quality Assessment Lab• Part 1 (Group): Exploring and Capturing Social Media Content and evaluating the overall quality

• Part 2 (Individual): Practical Impact for an Organization

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Social Media Extraction

• An API tool is developed• Search Query

• Specific Keywords

• Certain Locations

• Download Maximum of 900 Tweets from past 7 days

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Default Search Terms

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Intrinsic Information Quality (IIQ) Evaluations

No. Measure Description

1 Completeness

Extent to which the information is

not missing and is of sufficient

breadth and depth.

2 OriginalityHow much information are not

copied from other sources.

3 ObjectivityExtent to which information is

unbiased, unprejudiced.

4 Novelty If the information is innovative.

5 Accuracy

The degree to which data are

correct, reliable and free of errors

and is current.

6 Content quality

It includes semantic, syntactic,

grammar, punctuation and other

attributes of the text.

7 VerifiabilityThe degree to which information

can be checked for correctness.

8 ReliabilityExtent to which information is

correct and reliable.

Adopted from: Lee et al. 2002

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Results

• IIQ Map

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Student Assessment(before and after taking the social media lab exercise)

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1 No knowledge

2 Beginning knowledge

3 Intermediate knowledge

4 Very good knowledge

5 Advanced knowledge

Findings

• Consistent increase in student knowledge of Social Media business/technology information

• Large variation in social media data quality

• Importance of geo-tagged social media content

• Demonstrating practical use of social media for teaching and learning

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Query: Spatial IOT

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Specific Tweet - From Columbia

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GeoDev Germany Tweet from Palm Springs

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Query: California Budget (Geo-Tagged)

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Sample of Students Term Project

Target Use of RFID: MyLocate

• Searching the Twitter-verse for #RFID

• “In looking at our Twitter search, we were able to find the RFID Lab at Auburn University, which Target sponsors in addition to other sponsors like Amazon, FedEx, and Home Depot. Targets partnership with this Lab opens up many doors and opportunities for them to make this rollout and the MyLocate App possible.”

Project Done By: Heather Aguilar

Adeniyi Alade

Sharlene Hernandez

Cindy Montoya

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Conclusion

Acknowledgement

• Special Thanks to Academic Computing and Instructional Technology Services Grant, University of Redlands, 2017.

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References• Ali, M., Yaacob, R. A. I. B. R., Endut, M. N. A. A. B., & Langove, N. U. (2016). Strengthening the academic usage of social media: An exploratory study. Journal of King Saud University-Computer and Information Sciences.

• Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. harvard Business review Press.

• Griesemer, J. A. (2012). Using social media to enhance students’ learning experiences. Quality approaches in higher education, 3(1), 8-11.

• Hausmann, A., Toivonen, T., Slotow, R., Tenkanen, H., Moilanen, A., Heikinheimo, V., & Di Minin, E. (2017). Social Media Data Can Be Used to Understand Tourists’ Preferences for Nature‐Based Experiences in Protected Areas. Conservation Letters.

• Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & management, 40(2), 133-146.

• Menfors, M., & Fernstedt, F. (2015). Geotagging in social media: exploring the privacy paradox.

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