In their work Scenario-based e-learning: Evidence-based guidelines for online workforce learning, Clark defines Scenario-based e-learning and discusses its constituent parts. Most of the discussion however focuses on examples that are outside the domain of programming instruction. This brief exploration of Advent of Code as an instance of Scenario-based e-learning seeks to ground Clark's discussion to this area of instruction.
In the yearly Advent of Code event, a scenario is given and then there are guided steps, each with their own instructions, by which participants build a stepwise programming solution to the general scenario.
While there is no explicitly stated learning objective, the creator of Advent of Code describes it as
"Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. People use them as a speed contest
, interview prep , company training
, university coursework , practice problems, or to challenge each other."
Because there is no explicitly stated learning objective and participants can use the Advent of Code scenario to achieve their own desired ends, the guidance provided in the scenario changes Advent of Code from a "discovery learning" scenario to a "guided learning" scenario (Clark, 2012, p. 122).
In the 2021 instantiation, the scenario starts with the following prompt as the scenario's trigger event.
You're minding your own business on a ship at sea when the overboard alarm goes off! You rush to see if you can help. Apparently, one of the Elves tripped and accidentally sent the sleigh keys flying into the ocean!
Before you know it, you're inside a submarine the Elves keep ready for situations like this. It's covered in Christmas lights (because of course it is), and it even has an experimental antenna that should be able to track the keys if you can boost its signal strength high enough; there's a little meter that indicates the antenna's signal strength by displaying 0-50 stars.
Now that the scenario is established, the actual task description is given as "Your instincts tell you that in order to save Christmas, you'll need to get all fifty stars by December 25th.". Each day from December 1 to December 25, two correlated puzzles are offered that build off of the previous days' puzzles to guide participants towards a complete solution.
For the first day's first puzzle, scenario data in the form of a CSV file of depth readings is provided. This data file looks like:
The guidance and instruction for the first puzzle is then provided, in which it reads in part
The first order of business is to figure out how quickly the depth increases, just so you know what you're dealing with - you never know if the keys will get carried into deeper water by an ocean current or a fish or something. To do this, count the number of times a depth measurement increases from the previous measurement. (There is no measurement before the first measurement.) In the example above, the changes are as follows:
199 (N/A - no previous measurement)
This guidance, especially the inclusion of an example programming script output that illustrates part of the task, successfully mitigates the "flounder factor" by showing participants what is expected of them. Clark suggests that "One of the most important success factors in scenario-based e-learning is sufficient guidance to minimize the flounder factor" (2012, p. 30). In order to complete the first day's first puzzle, participants must write a programming script that results in an answer to the question of "How many measurements are larger than the previous measurement?". The provided response to this question is automatically checked by the scenario's own programming.
If an incorrect response is provided, the scenario provides feedback that is more than simply an indication of whether or not the response was correct. Clark writes that "feedback has little value unless the learner reviews the feedback and considers how his or her actions or decisions led to the outcomes seen" (2012, p. 81). This scenario applies this insight by providing formative feedback with additional guidance of
That's not the right answer; your answer is too low. If you're stuck, make sure you're using the full input data; there are also some general tips on the about page, or you can ask for hints on the subreddit."
The addition of hints such as "make sure you're using the full input data", guide participants to consider where and how their proposed solution deviates from the ideal solution based on the other bits of feedback such as "your answer is too low." This inclusion of feedback statements such as "your answer is too low", or "your answer is too high" ensures that the provided feedback is similar to what Clark refers to as intrinsic feedback in which there is a visual representation of "how the scenario plays out or responds to the learner's actions" (2012, p. 80).
This scenario does not have an explicit "reflection" phase, which doesn't detract from its effectiveness as an instantiation of Scenario-based e-learning. This is because "while some components, such as task deliverable, trigger event, and feedback, are required elements, others may vary according to your learning domain and context" (Clark, 2012, p. 72). In the context of this scenario, the only reflection is implicit in each days' puzzles building off each other and therefore each puzzle's solution must be adapted to become the solution for the next puzzle.
This scenario could be improved by providing a venue in which participants must explicitly reflect on their provided solution to a puzzle. Such an explicit venue could take the form of asking participants to remark on the space or time complexity of their solution (commonly referred to as "Big-O notation"), or on the elegance and structure of their solution's code. Given that the understated goal of the entire Advent of Code scenario is to enable participants to use programming code to solve a real-world problem, such reflection encourages participants to consider the real-world consequences of their particular solution.
Clark, R. C.(2012). Scenario-based e-learning: Evidence-based guidelines for online workforce learning (1st ed.). San Francisco, CA: Pfeiffer.