Bibliographic record
Abstract
The emergence of blockchain strikes at the heart of the AMCIS 2018 SIGED call, and also to the heart of the theme for AMICS 2018: Digital Disruption. The aim of this TREO talk is to discuss how we can introduce and integrate the topic of blockchain in the MIS curriculum. I will cover how to introduce the topic to the spectrum of MIS classes, how to unpack the many layers and nuances of blockchain, the depth vs breadth debate on how much and how deep to cover the topic, and finally how to integrate projects as well as the topic of bitcoin into the classroom. \\ \\ The prevalence of media coverage of bitcoin makes it an excellent entry point to a more nuanced discussion of blockchain. In most cases the discussion about bitcoin will start with “what is bitcoin and should I invest in it?” Instead I suggest starting with something that is more familiar for students: the purchase and sale of used cars. Students often understand a large number of the underlying attributes that would make it sensible to purchase a used car. From this I demonstrate that the same is just not true for bitcoin which leads us to the fact that we need to understand what supports bitcoin, which is blockchain. This "shall I invest in bitcoin" discussion can occur in almost every class in the MIS curriculum. \\ \\ To further unpack blockchain for class, we start discussions with the Avital et al. (2016) definition of blockchain, examining each word of the definition in turn and posing a set of directed questions that result from each word for a discussion which can fill one or two class periods. The breadth versus depth discussion will also be discussed. Kursch and Gold (2016) surveyed offerings of FinTech curriculum across several schools and found that there were “overview vs. specialized” offerings. Importantly, the “split between courses that provide a broad overview of the entire FinTech universe versus courses that examine one specialized aspect of FinTech (i.e. [sic], Bitcoin or cryptocurrency) is 50-50”. This suggests that there is no one way to present this topic correctly when it comes to blockchain education and the choice should be guided by the vision and strategy of your particular MIS department. \\ \\ To encourage hands-on learning, we suggest students set up in Ethereum as a useful way to help them understand blockchain issues. This setup can be accomplished by providing access to YouTube tutorial videos to help students accomplish this task. We have also found that students need four to five weeks to master concepts like blockchain so that they can effectively help local companies, which leaves too little time to actually work on blockchain projects for those companies. As a result, we do in-class projects to support learning which I will cover in the TREO talk. Finally, we discuss bitcoin and some of the technical aspects of how it works, including cryptomining. In class we discuss headlines such as “Canadian couple pours life savings into bitcoin mine” (CBC 2017). I will discuss my approach in the TREO talk. \\ \\ References \\ \\ Avital, M., Beck, R., King, J.L., Rossi, M., Teigland, R., 2016. “Jumping on the Blockchain Bandwagon : Lessons of the Past and Outlook to the Future,” Proceedings of the 36th International Conference on Information Systems. Atlanta, GA : Association for Information Systems. AIS Electronic Library (AISeL). Atlanta, GA \\ \\ CBC news, December 2017, http://www.cbc.ca/news/business/bitcoin-mine-canada-1.4436149 \\ \\ Kursh, S.R., Gold, N.A., 2016. “Adding FinTech and Blockchain to Your Curriculum,” Business Education Innovation Journal, Vol 8:2, December 2016, 1-12. \\
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".