Page Comparison

Definition

For OGHR, Platform needs to enable user to undertake exams. Platform should be capable of processing the user responses and evaluate these responses with relevant applicable scores. Outcome of this design should enable the platform users to take up examination with server side evaluation of results.

Background

In the present system, Question Set is processed as follows:

Entities

Assessment User : Active seeker of Assessment in the platform. Any Collection in Sunbird Ecosystem needs to be trackable in order to track the progress of the user. User’s state with the content is updated using Content State Update API. All of the Information on Content State Read API is retrieved against a Collection & User.

QuML Player : QuML Player has capability to play the questions in QuML format. It also has capability to locally validate the user input. Response Validation,Score Computing is completely handled in player. Once User Submits the overall response, client validated scores and response are updated using Content State Update API.

Flink Jobs : Flink Jobs aggregates the content state using Collection Activity Aggregator, Collection Assessment Aggregator, Cert Pre-processor, Cert Generator jobs.

Question Types:

a) MCQ (Multiple Choice Questions)

b) MSQ (Multiple Select Questions)

c) MTF (Match The Following)

d) FTB (Fill in The Blanks)

Excluded Types

a) VSA - (Very Short Answer)

b) SA - (Short Answer)

c) LA - (Long Answer)

Building Blocks :

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Building Blocks

...

API

...

Flink Jobs

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Sunbird InQuiry

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Question Set Hierarchy API

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Question List Read API

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QuestionSet Create API

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Question Create API

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Sunbird Lern

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Content State Read

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Collection Activity Aggregate

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Content State Update

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Collection Assessment Aggregate

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Enrollment List API

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Cert Pre Processer

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Cert Generator

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Sunbird RC

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Cert Registry Download API

Image Removed

Design PreRequisites :

Allow the response processing of question to be done on server rather than client as happening today.
Scalable Response Processing Solution for Question Sets.
Score Calculation Based on Content State Update.
Solution to the question needs to be masqueraded or excluded from the Question Read API/ Question List API.
Response Processing can happen in two ways:
- Entire Question Set Response Processing
- Question by Question Response Processing

Technical Design Details:

QuestionSet Hierarchy API : A new attribute in QuestionSet to introduced to have evaluated response declaration. (“evaluable“)

Code Block
"questionSet": { "timeLimits": "{\"maxTime\":\"3600\"}", "evaluable": true //#true for Server Side Valuation Default:#false for client side validation }

Question Read API : Any Question Associated with evaluable Question Set to trim off response declaration from Question Set. Instead a responseKey to be shared along with responseDeclaration.

Code Block

"evaluable": true,
"responseDeclaration": {
          "response1": {
            "maxScore": 1,
            "cardinality": "single",
            "type": "integer",
            -- To be Trimmed off ----
            "correctResponse": {
              "value": "0",
              "outcomes": {
                "SCORE": 1
              }
            },
            -- To be Trimmed off --
            #Newly Introduced Attribute
            "responseKey": "#Computed Hash Value of the result"
          }
        },

Question Creation Impact:
It is possible to permanently mark the question to be “evaluable“ in nature. Any Evaluable Question would automatically qualify for evaluable question Set. These Questions can be part of Evaluable QuestionSet only. Question Read of Evaluable Questions should exhibit the behaviour mentioned above.

Response Evaluation:

...

QuestionResponseValidateAPI:

API accepts the request payload similar to content state update API.

Code Block

{
  "request": {
    "userId": "843a9940-720f-43ed-a415-26bbfd3da9ef",
    "assessments":[
      {
        "assessmentTs": 1681284869464,
        "batchId": "0132677340746629120",
        "courseId": "do_213267731619962880127",
        "userId": "843a9940-720f-43ed-a415-26bbfd3da9ef",
        "attemptId": "5486724f41afb4997118e6d97695684f",
        "contentId": "do_2129959063404544001107"
        },
        responses:[{
          "identifier":"<question-id>",
          "questionType": "",
          "userResponse":[""],
          "responseKey":["<response-key>"]
        }]
      }],
    "contents": [
      {
        "contentId": "do_2132671468826214401203",
        "batchId": "0132677340746629120",
        "status": 2,
        "courseId": "do_213267731619962880127",
        "lastAccessTime": "2023-04-12 12:56:45:687+0530"
      },
      
    ]
  }
}

...

Definition

For OGHR, Platform needs to enable user to undertake exams. Question set evaluation is currently on the client side and this implies that the correct answer is available to the client. We need to introduce the capability to process the user responses, evaluate and compute scores on the server side so that the correct answer does not have to be sent to the client side.

...

Background

In the present system, Question Set consist of following :

Assessment User : Active seeker of Assessment in the platform.

QuML Player : QuML Player has capability to play the questions in QuML format. It also has capability to evaluate and compute score. Response Validation, Score Computing is completely handled in player as of now. Once User Submits the overall response, client validated scores and response are sent to the backend as ASSESS events using Content State Update API.

Question Editor : Question Set Editor enables the sourcing of question and questionSet in the system.

Flink Jobs : Flink Jobs aggregates the content state using Collection Activity Aggregator, Collection Assessment Aggregator, Cert Pre-processor, Cert Generator jobs.

Question Types

Objective Types:

a) MCQ (Multiple Choice Questions)

b) MSQ (Multiple Select Questions)

c) MTF (Match The Following)

d) FTB (Fill in The Blanks)

Subjective Types

a) VSA - (Very Short Answer)

b) SA - (Short Answer)

c) LA - (Long Answer)

Building Blocks :

Building Blocks	API	Flink Jobs
Sunbird InQuiry	Question Set Hierarchy API
Sunbird InQuiry	Question List Read API
	QuestionSet Create API
	Question Create API
Sunbird Lern	Content State Read	Collection Activity Aggregate
	Content State Update	Collection Assessment Aggregate
	Enrollment List API	Cert Pre Processer
		Cert Generator
Sunbird RC	Cert Registry Download API

...

Solution of Problem :

Allow the assessment evaluation to be done on server for question sets that are chosen to be server evaluable.
Answers to the question needs to be excluded from the Question Read API/ Question List API for server side assessment evaluation questions.

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Current Workflow

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Solution Proposed

Image Added

Technical Design Details:

Creation side Enhancements:

Introduce “eval“ : {“mode“ : “client/server“} attribute at question and questionSet Object Metadata level

A new attribute in QuestionSet to be introduced to mark it for server side evaluation. The editor need to allow setting this flag on a questionSet/questions. The creation APIs would be updated to support this attribute. Any question that is marked as “eval” : { “mode“ : “server“ } can only be part of a questionSet that is also marked as “eval” : { “mode“ : “server“ } . Also a question set that is marked “eval” : { “mode“ : “server“ } should only contain questions with “eval” : { “mode“ : “server“ }.

(“evaluate at server “).

Code Block

language	json

//Question Set Object
"questionSet": {
  “eval” : { “mode“ : “server“ } //#true for Server Side Valuation Default:#false for client side validation
}
//Question Object
"question" : {
  “eval” : { “mode“ : “server“ }
}

Consumption side Enhancements

There are two modes of accessing questionSet post this proposed change:
a) evaluable-mode : default ( In case eval attribute does not exist or mode = client)

In this mode, theplayer uses the new GET questionSetHierarchy to fetch the hierarchy, existing question list API to fetch question body and the existing content state update API to submit the ASSESS events. Calculation of assess score remains at client side.

b) evaluable-mode : server

Info
Content Compatibility needs to be set to higher value so that discovery on older clients dont happen for this questionSet

We are proposing that, in this mode, the player uses the new POST questionSetHierarchy to fetch the hierarchy, the existing question list API and the existing Content State Update API without passing “score” & “pass”. Content State Update will fetch “score“ & “pass“ using new Inquiry Assessment API which introduced as part of this feature.

QuestionSet Hierarchy API (new POST API)

The current questionsetHierarchy API is a get call and does not take in arguments. Introduce a new POST method for QuestionSet Hierarchy API that can take in request body. This API will have payload as follows

Code Block

language	json

{
    "request": {
        "questionset": {
            "contentID": "",
            "collectionID": "",
            "userID": "",
            "attemptID": ""
        }
    }
}

This API would handle shuffling of options , selection of a subset of questions and randomisation (currently done by player) as indicated by the metadata in the questionSet. The API will also return a “QuestionSetToken“ which is a signed token contains user-id, content-id, collection-id,attempt-id+selected_questionid_list received , eval mode as part of hierarchy payload. This token will further be passed to Content State Update & to new submitAssessment API from Content State Update.“QuestionSetToken“ will be validated by submitAssessment API call.

Code Block

language	json

"questionSet": {
  "timeLimits": "{\"maxTime\":\"3600\"}",
  "questionSetToken": "", //#Question Set token to be generated at hierarchy read API with combination of "Question Set ID + userID"
  “eval” : { “mode“ : “server“ } //#true for Server Side Valuation Default:#false for client side validation
}

Info

QuestionSetToken : This key is almost equivalent of jwt token created as follows:

{
"data": " {

"contentID":"do_11381896937577676811",

"evalMode":"server",

"collectionID":"do_11381130283086643213",

"userID":"user-id",

"questionList":"<do-id-1>,<do-id-2>,….<do-id-n>",

"attemptID":"attempt-id"

}

2. Updates to QuestionList API

Question Read API : Any Question Associated with eval-mode-server behaviour to trim off response declaration from Question Set and other answer displayed reference parameters in the response.

Code Block

"serverEvaluable": true,
"responseDeclaration": {
          "response1": {
            "maxScore": 1,
            "cardinality": "single",
            "type": "integer",
            -- To be Trimmed off ----
            "correctResponse": {
              "value": "0",
              "outcomes": {
                "SCORE": 1
              }
            },
            -- To be Trimmed off --
           
          }
        },

There are multiple attributes which persists correct answer in QuML

a) responseDeclaration: (Shown above)

b) answer

c) editorState

3. Assessment API to evaluate user responses & calculate score.

QuestionResponseValidateAPI (Sync API Behaviour):

QuestionSetToken generated in Hierarchy is sent as part of this request. This token will help validate that the responses are submitted for the questions that were given out to this user and also verify the time of submission.

API accepts the request payload similar to content state update API.

Code Block

{
  "request": {
    "userId": "843a9940-720f-43ed-a415-26bbfd3da9ef",
    "questionSetToken": "",
    "assessments":[
      {
        "assessmentTs": 1681284869464,
        "batchId": "0132677340746629120",
        "collectionId": "do_213267731619962880127",
        "userId": "843a9940-720f-43ed-a415-26bbfd3da9ef",
        "attemptId": "5486724f41afb4997118e6d97695684f",
        "contentId": "do_2129959063404544001107"
        },
        "events":[],
      }],
    "contents": [
      {
        "contentId": "do_2132671468826214401203",
        "batchId": "0132677340746629120",
        "status": 2,
        "courseId": "do_213267731619962880127",
        "lastAccessTime": "2023-04-12 12:56:45:687+0530"
      },
      
    ]
  }
}

The Above payload mimics ContentStateUpdate API to mimic responses as above.

Expand

title	Reference Call for Content State Update

Code Block

curl 'https://staging.sunbirded.org/content/course/v1/content/state/update' \
  -X 'PATCH' \
  -H 'Accept: application/json' \
  -H 'Accept-Language: en-GB,en-US;q=0.9,en;q=0.8' \
  -H 'Connection: keep-alive' \
  -H 'Content-Type: application/json' \
  -H 'Cookie: connect.sid=s%3Ahiv7abkP2ptQEspxLosgzBh_WeQAMcyt.Xx9l7ib6kHeMr131BvH4SHBzcXlcenr6BwIMAe1%2FVzo' \
  -H 'Origin: https://staging.sunbirded.org' \
  -H 'Referer: https://staging.sunbirded.org/learn/course/play/do_213267732169023488128?batchId=0132677340746629120&courseId=do_213267731619962880127&courseName=April%20course%203.9&selectedContent=do_2129959063404544001107' \
  -H 'Sec-Fetch-Dest: empty' \
  -H 'Sec-Fetch-Mode: cors' \
  -H 'Sec-Fetch-Site: same-origin' \
  -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36' \
  -H 'X-App-Id: staging.sunbird.portal' \
  -H 'X-App-Version: 5.2.0' \
  -H 'X-Channel-Id: 0126796199493140480' \
  -H 'X-Device-ID: dfab80b18d8ce5d4df8159ba05dde951' \
  -H 'X-Org-code: 0126796199493140480' \
  -H 'X-Request-ID: 0d460871-be9d-adf1-f7ab-aac64d1eaff7' \
  -H 'X-Session-ID: hiv7abkP2ptQEspxLosgzBh_WeQAMcyt' \
  -H 'X-Source: web' \
  -H 'X-User-ID: 843a9940-720f-43ed-a415-26bbfd3da9ef' \
  -H 'X-msgid: 0d460871-be9d-adf1-f7ab-aac64d1eaff7' \
  -H 'sec-ch-ua: "Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"' \
  -H 'sec-ch-ua-mobile: ?0' \
  -H 'sec-ch-ua-platform: "macOS"' \
  -H 'ts: 2023-04-12T13:05:05+05:30' \
  --data-raw '{"request":{"userId":"843a9940-720f-43ed-a415-26bbfd3da9ef","contents":[{"contentId":"do_2129959063404544001107","batchId":"0132677340746629120","status":2,"courseId":"do_213267731619962880127","lastAccessTime":"2023-04-12 13:05:05:524+0530"}],"assessments":[{"assessmentTs":1681284869464,"batchId":"0132677340746629120","courseId":"do_213267731619962880127","userId":"843a9940-720f-43ed-a415-26bbfd3da9ef","attemptId":"5486724f41afb4997118e6d97695684f","contentId":"do_2129959063404544001107","events":[{"eid":"ASSESS","ets":1681284888762,"ver":"3.1","mid":"ASSESS:67cf520cf4d9a29473844c19c3c3813d","actor":{"id":"843a9940-720f-43ed-a415-26bbfd3da9ef","type":"User"},"context":{"channel":"0126796199493140480","pdata":{"id":"staging.sunbird.portal","ver":"5.2.0","pid":"sunbird-portal.contentplayer"},"env":"contentplayer","sid":"hiv7abkP2ptQEspxLosgzBh_WeQAMcyt","did":"dfab80b18d8ce5d4df8159ba05dde951","cdata":[{"id":"do_213267732169023488128","type":"course"},{"type":"batch","id":"0132677340746629120"},{"id":"af588b6a747b6b3d28bea6d947dfbc49","type":"ContentSession"},{"id":"1a20fb335ff8dada7802d4473fe1a55e","type":"PlaySession"}],"rollup":{"l1":"0126796199493140480"}},"object":{"id":"do_2129959063404544001107","type":"Content","ver":"2","rollup":{"l1":"do_213267732169023488128","l2":"do_2129959063404544001107"}},"tags":["0126796199493140480"],"edata":{"item":{"id":"do_21299582901864857613016","maxscore":1,"type":"ftb","exlength":0,"params":[{"1":"{\"text\":\"\"}"},{"2":"{\"text\":\"\"}"},{"3":"{\"text\":\"\"}"},{"eval":"order"}],"uri":"","title":"Registration","mmc":[],"mc":[],"desc":""},"index":1,"pass":"No","score":0,"resvalues":[{"1":"{\"text\":\"Sharath\"}"},{"2":"{\"text\":\"10\"}"},{"3":"{\"text\":\"CSE\"}"}],"duration":16}},{"eid":"ASSESS","ets":1681284892463,"ver":"3.1","mid":"ASSESS:e57069e81c8e561915b20de23969e7ab","actor":{"id":"843a9940-720f-43ed-a415-26bbfd3da9ef","type":"User"},"context":{"channel":"0126796199493140480","pdata":{"id":"staging.sunbird.portal","ver":"5.2.0","pid":"sunbird-portal.contentplayer"},"env":"contentplayer","sid":"hiv7abkP2ptQEspxLosgzBh_WeQAMcyt","did":"dfab80b18d8ce5d4df8159ba05dde951","cdata":[{"id":"do_213267732169023488128","type":"course"},{"type":"batch","id":"0132677340746629120"},{"id":"af588b6a747b6b3d28bea6d947dfbc49","type":"ContentSession"},{"id":"1a20fb335ff8dada7802d4473fe1a55e","type":"PlaySession"}],"rollup":{"l1":"0126796199493140480"}},"object":{"id":"do_2129959063404544001107","type":"Content","ver":"2","rollup":{"l1":"do_213267732169023488128","l2":"do_2129959063404544001107"}},"tags":["0126796199493140480"],"edata":{"item":{"id":"do_21299582796158566413015","maxscore":1,"type":"ftb","exlength":0,"params":[{"1":"{\"text\":\"\"}"},{"eval":"order"}],"uri":"","title":"Edit test UT","mmc":[],"mc":[],"desc":""},"index":2,"pass":"No","score":0,"resvalues":[{"1":"{\"text\":\"1\"}"}],"duration":4}}]}]}}' \
  --compressed

...

Question Set Response Processing flow

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Future Reference :

Questions List Validation using back-end persistence

Current design validity check of assigned Questions from QuestionSet collection proposes assigned list of questionIds to be added in the JWT during hierarchy call & validate same during New Inquiry Assessment API. Storing same in Redis & Cassandra is an option which currently not considered.

Scalable server side response processing for Question Sets.
Response Processing can happen in two ways:
- Entire Question Set Response Processing (Current solution scope)
- Question by Question Response Processing

Versions Compared

Old Version 29

New Version Current

Key

Definition

Background

Entities

Design PreRequisites :

Definition

Background

Solution of Problem :

Current Workflow

Solution Proposed

Technical Design Details:

Creation side Enhancements:

Consumption side Enhancements

QuestionResponseValidateAPI (Sync API Behaviour):

Future Reference :

Questions List Validation using back-end persistence