MGMT 1601: Management Software and Technology
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MGMT 1601: Management Software and Technology
Take-Home Intensive #2
Case: The Dalhousie Course Advisor
In September 2020, Dalhousie University was a strange place. Following a messy transition to online learning triggered by sudden government-enforced social distancing, the university was planning for an entire year of online course delivery for the first time in its history. Many decisions were made to support a positive online student experience. One of these decisions was to adopt the Dal Mobile app, an integrated university-wide social media platform for sharing social content. The early rollout of the app was bumpy because many students did not use the app’s social functions. However, those who did saw the app as beneficial (Conrad et al., 2022).
Once social distancing came to an end, the university kept the application in part because the app allowed the university to develop new features for engaging with students. For example, the Dalhousie IT team developed a course calendar feature, which is very widely used. The app is also one of the best ways to contact students and connect them to opportunities, such as campus jobs and services. The app provides campus maps which have helped students navigate the university’s buildings and find rooms.
One of the possible features which has not yet been built is an interactive academic advising chatbot. Students often prefer to connect to services through a mobile application and often struggle to connect to live advising services because they are either overbooked or difficult to access. For example, to access academic advice on which courses to take, a student would first have to decide whether they will seek the services from a professional advisor or seek it from other students on the web, such as on Reddit or WhatsApp. If they decide to go with the professional advisor, they will then have to find the advisor’s email on Dalhousie’s website, contact the advisor, wait for a response, coordinate with the advisor to find a time, and thengo to the advising session. This is a painful process that can take hours of a student’stime.
However, using OpenAI’s GPT-4 technology, together with some data provided by the university, it is possible to make DalBot, a reliable interactive chatbot which can provide students advice on which courses to take. The DalBot can be integrated with the Dal Mobile app and accessed by pressing a button. Students can enter their questions, and the DalBot will answer with advice that is sound for most students, most of the time. The problem with a technology like DalBot is that it is new and untested, which requires careful design considerations. If the university is going to spend thousands of dollars developing the DalBot, it should first know whether this is valuable to students.
To help with this, the Cognition and Organizations Research Group (CORG) is going to prototype and research this concept. We have been discussing this idea for a while and have developed a web-based prototype of the app’s front end, but it is broken and needs to be fixed. We identified the following problems:
1. The HTML document is not correctly connected to the CSS document.
2. The user logo in the chat is not configured correctly.
3. The “Submit” button is not spelled correctly.
4. The “Submit” button should be a blue color but it is currently not.
5. The “edited by” text at the bottom currently says “Colin”. Change this to your name so that we know that you edited it.
Please fix these issues. We also need your help with the design and vision of the DalBot because you, as a potential user of this feature, likely know whether this is a good idea better than we do. Though this web page does not simulate the mobile application, we believe it would be useful for gathering feedback from students.
What to submit
Using what you know about HTML, CSS, and the human design process, help our research group develop our prototype. You have been given a series of files that you must modify such that they reflect a functional prototype and an illustrated value proposition canvas. You must submit a single PDF document which contains the following:
1. A screenshot of your finished index.html website, opened in a browser (not your editor).
2. A finished value proposition canvas which illustrates the components of the value proposition and customer segments, from your perspective.
3. A written explanation of your value proposition canvas and a recommended approach to validating this design, which is no more than 500 words long.
In your written submission, be sure to explain whether the DalBot is likely to help Dalhousie students. It is fine to assess this positively or negatively, though you must take a position. You have also been provided with a rubric which explains how your submission will be evaluated.
Hints:
• You can find the HTML and CSS document in the ZIP folder provided with this document on Brightspace. You should unzip the folder before editing the files.
• Review the Value Proposition Canvas video or watch it if you haven’t seen it.
• Be sure to download and configure your PowerPoint file.
• You will have limited support for this exercise. If you get stuck on the HTML/CSS, you can check out W3Schools or can move on to the other components. Dr. Conrad will respond to emails but might not answer your questions, or will only answer generally.
References
Conrad, C., Moylan, R., & Diaz, G. O. (2022). University life has gone digital: Influences of institutional mobile social network use during the COVID- 19 emergency. Library Hi Tech, ahead of print.
Evaluation
You will be evaluated based on a rubric like the following:
|
Component |
Proficient (80% to 100%) |
Sufficient (65% to 79.9%) |
Marginal (50% to 64.9%) |
Insufficient (below 50%) |
|
Website (4 points) |
The website image reflects polished HTML and CSS code, and clearly solves all of the mentioned issues. |
The website image reflects correctly configured HTML and CSS code, and evidently solves many of the mentioned issues. |
The website image reflects edited HTML and CSS code, and appears to solve some of the mentioned issues. |
The website image does not reflect meaningfully edited HTML and CSS code. |
|
Value Proposition Canvas (4 points) |
The canvas accurately depicts the components of the customer segments and value proposition concepts. |
The canvas depicts the components of the customer segments and value proposition concepts. |
The canvas depicts some of the components of the customer segments and value proposition concepts. |
The canvas does not depict the components of the customer segments and value proposition concepts. |
|
Written assessment (7 points) |
The value proposition canvas is well- explained and sound explanations of your suggested customer discovery approach are provided. The response is well- written and free of spelling or grammar mistakes. |
The value proposition canvas is explained and explanations of your suggested customer discovery approach are provided. The response is largely free of spelling or grammar mistakes. |
The value proposition canvas is explained, though inaccurately, or a poor customer discovery approach is given. |
The written response does not adequately address the value proposition canvas or customer discovery approaches. |
2023-12-07