analyze_session takes a request body and returns an aggregated analysis of the user's explanations on all questions, along with similar topics for further exploration if the student achieves an average score of 3.5+ or easier topics otherwise.
To do:
Create an image for the session using Dall E API and store in Azure Blob Storage
{
"qualitative_analysis":{
"overall_comment":"You've done an excellent job in conveying complex computer science concepts clearly and accurately, maintaining a good balance between depth and accessibility. Your ability to explain with examples and analogies has significantly enhanced understanding, showcasing your strong grasp of the topics discussed.",
"strengths":"Your explanations stand out for their accuracy and clarity, particularly in breaking down intricate processes like merge sort and depth-first search into understandable segments. You've effectively used examples to illustrate these processes, making the concepts accessible to an undergraduate audience. Additionally, your discussions are comprehensive, covering key aspects and comparisons with related algorithms, which shows a deep understanding of the subjects.",
"room_for_improvement":"There's an opportunity to refine your explanations for greater conciseness and to expand your use of exemplification. While your examples are effective, incorporating a wider range of scenarios and simplifying technical language without losing detail could make your explanations even more engaging and understandable.",
"suggestions_for_improvement":"To further enhance your explanations, consider the following: 1. Work on condensing your discussions where possible, avoiding repetition and focusing on the essence of the concept. 2. Integrate more varied and detailed examples, especially for complex steps or concepts, to simplify understanding. 3. Continue to adapt your language and examples to match the audience's level, simplifying technical jargon without compromising the depth of your explanation. These adjustments will help make your already strong explanations even more impactful and accessible."
},
"scores":{
"clarity_score":4.0,
"conciseness_score":3.5,
"comprehensiveness_score":4.0,
"correctness_score":5.0,
"exemplification_score":3.5,
"adaptation_score":4.0,
"overall_score":4.0
},
"suggested_topics":[
"Greedy Algorithms",
"Dynamic Programming",
"Graph Algorithms",
"Divide and Conquer Algorithms",
"Hashing Algorithms"
],
"satisfactory_outcome":true
}
Seed data used for DB:
{
"concept": "algorithms",
"session_id": "test1",
"user_id": "KenjiPcx",
"student_persona": "Undergraduate level student",
"session_transcripts": [
{
"question_id": 2345,
"question": "What is Merge Sort",
"question_transcript": [
{
"user": "merge sort",
"assistant": "(Learner): Oh, merge sort sounds interesting! I've heard it's a way to sort items in a list, but I'm not quite sure how it works. Could you start by explaining what merge sort is and why it's used?"
},
{
"user": "You are right, it is a way to sort numbers in a list, you start by dividing a list of numbers into new lists until you have lists of just one number. So if you have [3,5,2,1] you would divide this until you have [[3],[5],[2],[1]]. When you do this, you can imagine a tree forming. Essentially as you divide the tree in half, you have 2 lists on the second level, and as you keep dividing, you end up with a list of lists with just 1 item. Then you can fall back onto the previous level by merging the two sorted lists back to the original list, and you do this until you get back the original list.",
"assistant": "(Learner): Wow, I think I'm starting to get it! So, we split the list into smaller lists until each one has just one number. This makes me wonder, how do we go from these individual lists back to a full, sorted list? Could you explain a bit more about how the merging process works after splitting the list?"
},
{
"user": "Suppose you have [[3],[5],[2],[1]], [3] and [5] was obtained by splitting [3,5] into 2 lists, so when we merge them back, we just need to merge these two lists that are sorted. In this case 3<5 so 3 comes before 5 in the new list. You do this for all the lists, and you should get 2 lists of 2 elements, then you will repeat this again, and then you will get the original list. But the second time you merge, you will realise that there are more than 2 elements on each list. But you would realise that they are already sorted up to the point of merging. So what you want to do is to have 2 pointers, one pointing at the start of each list and going through both lists. so that you can merge them into a new list. With these two pointers, you can compare and check which number from the both lists is smaller, then you add that number into the list. But once you've done that, you want to move the pointer away, and keep doing that until you have added all the numbers.",
"assistant": "(Learner): Oh, I see! So, by using pointers, we can keep track of which elements we've already considered in each of the smaller, sorted lists, and then we carefully merge these lists together by comparing their elements one by one. It's like weaving two sorted strands together into a single, sorted strand, right? I'm curious, how do we handle the situation where one list runs out of elements before the other? What happens to the remaining elements in the longer list during the merge process?"
},
{
"user": "Yeap, so when that happens you just add the rest of the list that is not being added to the list because at that pointer these remaining numbers are just smaller than all the added numbers, and remember they are sorted so it works",
"assistant": "(Learner): Oh, that makes sense! Because the lists we are merging are already sorted, if one list runs out of elements first, we can simply append the remaining elements from the other list directly to the end of the merged list, knowing they're in the correct order. It's like adding the last pieces to complete a puzzle. This process of dividing, sorting individually, and then merging back together ensures the entire list is sorted in the end. I think I understand the basic idea of merge sort now! It's a very systematic way to sort a list. Is there a specific reason why merge sort is preferred over other sorting methods in certain situations?"
},
{
"user": "Yes, merge sort is more efficient than the other sorting algorithms like insertion sort or selection sort. Formally this is known as time complexity, and merge sort has a big O of nlogn, as opposed to n^2 for the other sorting algorithms.",
"assistant": "(Learner): Ah, time complexity! That's about how long an algorithm takes to complete its task based on the size of its input, right? So, if merge sort has a time complexity of \\(O(n \\log n)\\), it means that as the list of numbers gets bigger, merge sort can sort the list more quickly than those other sorts with a time complexity of \\(O(n^2)\\), especially when the lists are very large. It's fascinating how the way we sort can make such a big difference in efficiency. This makes me wonder, are there scenarios where merge sort might not be the best choice, even with its impressive time complexity?"
},
{
"user": "Merge sort does not sort the numbers in place, meaning you need to create new lists which requires memory. If you don't want to use memory then you might want to consider heapsort, which has the same time complexity but it does not use extra memory",
"assistant": "(Learner): Oh, I see! So, while merge sort is efficient in terms of time, it requires extra memory because it has to create new lists during the sorting process. This trade-off between time efficiency and memory usage is really important to consider, especially in situations where memory is limited. Heapsort, on the other hand, sorts the list in place, saving memory but still maintaining that efficient \\(O(n \\log n)\\) time complexity. It's like choosing between two tools, where each has its own advantages depending on the job at hand. This really highlights the importance of understanding different algorithms and their characteristics. Thanks for explaining merge sort and its considerations so clearly! Is there another topic or concept you'd like to explore or explain?"
}
]
},
{
"question_id": 2346,
"question": "What is DFS",
"question_transcript": [
{
"user": "Depth first search is a very useful algorithm for graph and tree problems...",
"assistant": "That's an interesting way to visualize it! So, if I understand correctly, depth-first search goes deep into one path as far as it can before backtracking to explore other paths..."
},
{
"user": "Rewind is basically backtracking. It stops searching when you have found your desired node...",
"assistant": "Ah, I see! So, backtracking means it goes back to the previous node to explore other paths if the current path doesn't lead to the desired node..."
},
{
"user": "DFS consist of a stack, essentially, the first time you visit a node, you add the to the stack...",
"assistant": "Oh, I understand now! The stack is used to keep track of the nodes that the algorithm has visited..."
},
{
"user": "If you write an iterative algorithm, you can use a stack and add nodes to the stack...",
"assistant": "That's a clear distinction! So, in the iterative version, the stack is a data structure that we manage explicitly to keep track of the nodes..."
},
{
"user": "Yes you are right, it does not require manually managing the stack, and the recursive does not involve an explicit data structure.",
"assistant": "That makes sense. Using the recursive method seems convenient because of its simpler implementation, relying on the language's call stack..."
},
{
"user": "Yes, stack overflow is potentially an issue because you could reach a maximum recursive depth...",
"assistant": "Got it! That explains why someone might prefer the iterative approach despite the extra work of manually managing the stack..."
}
]
}
],
"session_analysis": [
{
"clarity_score": "4",
"clarity_explanation": "Your explanation of merge sort was very clear, particularly in how you described the process of dividing the list into smaller lists and then merging them back together with examples. The analogy of a tree and the use of pointers were effective in illustrating your points. However, there could be minor improvements in simplifying some of the technical language used to describe the merging process to enhance understanding further.",
"conciseness_score": "4",
"conciseness_explanation": "You were concise in your explanation, managing to convey the key aspects of merge sort efficiently. There was a good balance between detail and brevity, ensuring that the learner could follow along without getting overwhelmed by information. Minor areas, such as the detailed steps of the merge process, could be condensed slightly without losing clarity.",
"comprehensiveness_score": "4",
"comprehensiveness_explanation": "Your explanation covered all key aspects of merge sort, including the process of dividing and merging, the concept of time complexity, and comparisons with other sorting algorithms. You also addressed a potential drawback regarding memory usage. Providing more examples or addressing additional edge cases could offer a fuller understanding but overall, the explanation was comprehensive.",
"correctness_score": "5",
"correctness_explanation": "All the information you provided about merge sort was accurate, reflecting a deep understanding of the concept. You correctly explained the algorithm's process, its efficiency in terms of time complexity, and how it compares to other sorting methods. Your discussion on the trade-offs between merge sort and heapsort was also precise.",
"exemplification_score": "4",
"exemplification_explanation": "You used several well-chosen examples to illustrate the merge sort process, such as dividing a list into smaller lists and the use of pointers during the merge process. These examples significantly enhanced understanding. Integrating additional examples, especially to explain complex steps in simpler terms, could further improve comprehension.",
"adaptation_score": "4",
"adaptation_explanation": "You did well in adapting your explanation to the undergraduate level, using appropriate terminology and complexity for the audience. You provided analogies and examples that were accessible, though at times, the technical language could be slightly simplified. Minor improvements in this area could make the explanation accessible to a broader audience while still engaging those with more background knowledge.",
"question": "What is Merge Sort",
"question_id": 2345
},
{
"clarity_score": "4",
"clarity_explanation": "Your explanations were generally clear, with a logical flow that builds upon each point. For instance, describing depth-first search as going deep into one path before backtracking and then explaining the role of the stack in this process was well-done. However, there were moments where simplifying the language further or breaking down concepts more granely could have enhanced clarity, particularly for an undergraduate audience who might not be familiar with all technical terms.",
"conciseness_score": "3",
"conciseness_explanation": "Your explanation strikes a good balance between detail and brevity. You were efficient in conveying the key points about the depth-first search, its implementation, and potential issues like stack overflow. Nevertheless, there were instances where the explanation could be streamlined for greater impact. For example, integrating the discussion about the stack in both recursive and iterative methods more succinctly could avoid slight repetition and maintain focus.",
"comprehensiveness_score": "4",
"comprehensiveness_explanation": "You did a commendable job covering the key aspects of depth-first search, including its basic principle, the use of a stack, the distinction between recursive and iterative approaches, and the mention of stack overflow. This provides a comprehensive understanding of the topic. However, including more varied examples or discussing applications of depth-first search in real-world scenarios could have further enriched the comprehensiveness of your explanation.",
"correctness_score": "5",
"correctness_explanation": "All the information you provided about depth-first search and its implementation details was accurate and correctly presented. You correctly distinguished between the recursive and iterative approaches and appropriately highlighted the potential issue of stack overflow in recursive implementations. This demonstrates a solid understanding of the concept.",
"exemplification_score": "3",
"exemplification_explanation": "You utilized examples to illustrate your points, such as the use of a stack for tracking nodes, which helped in understanding the depth-first search process. However, the examples were somewhat basic and could be expanded upon for greater clarity and engagement. Incorporating more detailed scenarios or problems where depth-first search is applied could have made your explanation more vivid and illustrative.",
"adaptation_score": "4",
"adaptation_explanation": "You did well adapting your explanation to an undergraduate audience, carefully balancing technical detail with accessibility. You introduced concepts in a logical order and provided enough context for someone with a foundational understanding of computer science concepts. Minor improvements, such as further simplifying technical jargon or providing more basic examples, could make your explanations even more accessible to all undergraduates, regardless of their prior exposure to the topic.",
"question": "What is DFS",
"question_id": 2346
}
],
}
Code changes
analyze_session_api(get_analysis_by_sessionAPI description
analyze_sessiontakes a request body and returns an aggregated analysis of the user's explanations on all questions, along with similar topics for further exploration if the student achieves an average score of 3.5+ or easier topics otherwise.To do:
Data description
Input:
Output:
Seed data used for DB: