Skills Audit for Maths and Stats - Management and Creative and Cultural Industries (MGT6043)

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Will Morgan

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From users who are members of Skills Audits for Maths and Stats :

Will Morgan

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Ready to use

8 months, 1 week ago

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Will Morgan 8 months, 1 week ago

Gave some feedback: Ready to use

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Will Morgan 9 months ago

Gave some feedback: Needs to be tested

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Will Morgan 9 months ago

Published this.

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Will Morgan 9 months ago

Created this as a copy of Skills Audit for Maths and Stats - Occupational Psychology and Work Psychology (MGT6078).

Name	Status	Author	Last Modified
Skills Audit for Maths and Stats - Accounting and Financial Management (and duals) (MGT 143)	Ready to use	Will Morgan	03/07/2024 13:58
Skills Audit for Maths and Stats - Business management (and duals) (MGT134)	Ready to use	Will Morgan	03/07/2024 13:35
Skills Audit for Maths and Stats - Occupational Psychology and Work Psychology (MGT6078)	Ready to use	Will Morgan	10/07/2024 10:17
Skills Audit for Maths and Stats - Management and Creative and Cultural Industries (MGT6043)	Ready to use	Will Morgan	10/07/2024 10:23
Skills Audit for Maths and Stats - MBA (MGT6111)	Ready to use	Will Morgan	10/07/2024 10:20
Skills Audit for Maths and Stats - International Management and Marketing, and Marketing Management Practice (MGT6250)	Ready to use	Will Morgan	10/07/2024 12:22
Skills Audit for Maths and Stats - International Management (MGT6252)	Ready to use	Will Morgan	10/07/2024 11:19
Skills Audit for Maths and Stats - Finance and Accounting (MGT6097)	Ready to use	Will Morgan	10/07/2024 12:24
Skills Audit for Maths and Stats - HRM (MGTT67 & MGTT199)	Ready to use	Will Morgan	10/07/2024 12:15
Skills Audit for Maths and Stats - Logistic and Supply Chain Management (MGTT167)	Ready to use	Will Morgan	10/07/2024 12:22
Skills Audit for Maths and Stats - Accounting, Governance and Financial Management (MGTT192)	Ready to use	Will Morgan	10/07/2024 12:25

There is one other version that you do not have access to.

Question groups

Questions 40 questions

1.
Ready to use

Metric Unit conversion - division by 1000.
2.
Ready to use

convert from inches to cm.
3.
Ready to use

Calculating the LCM and HCF of numbers by using prime factorisation.
4.
Ready to use

Questions testing understanding of the precedence of operators using BIDMAS, applied to integers. These questions only test DMAS. That is, only Division/Multiplcation and Addition/Subtraction.
5.
Ready to use

Round numbers to a given number of significant figures.
6.
Ready to use

State the Upper and lower bound of a distance that has been rounded to either the nearest 10 or 100 miles.
7.
Ready to use

Calculations with negative numbers.
8.
Ready to use

Calculations with negative numbers.
9.
Ready to use

Compound percentage change.
10.
Ready to use

Find the original price before a discount by dividing the new price by the percentage discount.
11.
Ready to use

Manipulate fractions in order to add and subtract them. The difficulty escalates through the inclusion of a whole integer and a decimal, which both need to be converted into a fraction before the addition/subtraction can take place.
12.
Ready to use

This question tests the student's ability to identify equivalent fractions through spotting a fraction which is not equivalent amongst a list of otherwise equivalent fractions. It also tests the students ability to convert mixed numbers into their equivalent improper fractions. It then does the reverse and tests their ability to convert an improper fraction into an equivalent mixed number.
13.
Ready to use

Dividing amounts in ratios
14.
Ready to use

Calculations involving Standard form.
15.
Ready to use

Calculate an answer involving a fractional index.
16.
Ready to use

perform a calculation involving negative indices.
17.
Ready to use

Expanding two linear brackets multiplied together.
18.
Ready to use

Solve linear equations with unkowns on both sides. Including brackets and fractions.
19.
Ready to use

Solving a pair of linear simultaneous equations, giving answers as integers or fractions.
20.
Ready to use

Rearrange a specific formula. No randomisation.
21.
Ready to use

Factorise three quadratic equations of the form $x^2+bx+c$ . The first has two negative roots, the second has one negative and one positive, and the third is the difference of two squares.
22.
Ready to use

Solving a quadratic equation via factorisation (or otherwise) with the $x^2$ -term having a coefficient of 1.
23.
Ready to use

Simplify (qx+a)/(rx+b) +/- (sx+c)/(tx+d) x is a randomised variable. a,b,c,d,q,r,s,t are randomised integers. a,b,c,d run from -5 to 5, including 0. q,r,s,t run from -3 to 3, and can be 0 if the constant term is nonzero, but are mostly 1.
24.
Ready to use

Manipulate fractions in order to add and subtract them. The difficulty escalates through the inclusion of a whole integer and a decimal, which both need to be converted into a fraction before the addition/subtraction can take place.
25.
Ready to use

Calculate the volume of different 3D shapes, given the units and measurements required. The formulae for the volume of each shape are available as steps if required.
26.
Ready to use

Find the diagonal or one side of a rectangle using Pythagoras' theorem.
27.
Ready to use

Draws a triangle based on 3 side lengths. Randomises asking angle or side.
28.
Ready to use

Calculating gradient and finding intercept from a geogebra graph.
29.
Ready to use

Decide whether each of the described sets of data is drawn from a discrete or continuous distribution.
30.
Ready to use

This question is about identifying what types of charts or visual representations of data you can use for different data sets.
31.
Ready to use

This question is about correctly interpreting pie charts.
32.
Ready to use

Reading a value from a simple bar chart.
33.
Ready to use

Interpreting the elements of a box plot
34.
Ready to use

Calculating the Mean from a basic list of integers.
35.
Ready to use

This question provides a list of data to the student. They are asked to find the "mode".
36.
Ready to use

This question provides a list of data to the student. They are asked to find the "median".
37.
Ready to use

Identifying measures of spread or location (average)
38.
Ready to use

Tests understanding of scatter plots and related concepts.
39.
Ready to use

Calculate probability of selecting coloured counters from a bag.
40.
Ready to use

Predicting the probability of an unbiased coin landing on heads based on the results of previous throws.

Topics

No topics have been defined.

Diagnostic algorithm

Extend built-in algorithm?

Base diagnostic algorithm

state (Produces the initial value of the state object): // should be renamed "initial_state"
    [
        "topics": map(
            [
                "topic": topic,
                "status": "unknown" // "unknown", "passed", or "failed"
            ],
            topic,
            values(topics)
        ),
        "retries": 3,
        "finished": false,
    ]

topics_by_objective (A dictionary mapping a learning objective name to a list of indices of topics):
    dict(map(
        let(
            ltopics, values(topics),
            indices, filter(lo["name"] in ltopics[j]["learning_objectives"], j, 0..len(ltopics)-1),
            [lo["name"],indices]
        ),
        lo,
        learning_objectives
    ))

unknown_topics (Which topics are still unknown?): 
    map(x["topic"],x,filter(x["status"]="unknown",x,state["topics"]))

first_topic (The first topic to pick a question on):
    unknown_topics[floor(len(unknown_topics)/2)]["name"]

first_question (The first question to show the student):
    random(topics[first_topic]["questions"])

get_dependents (An expression which gets the topics to update after answering a question):
    expression("""
        [target] + flatten(map(eval(get_dependents,["target":t,"correct":correct]),t,topics[target][if(correct,"depends_on","leads_to")]))
    """)

correct (Did the student get the current question right?):
    current_question["credit"]=1

after_answering (Update the state after the student answers a question):
    let(
        ntopics, eval(get_dependents,["target":current_topic,"correct":correct])
    ,   nstate, state + ['topics': map(
                    if(tstate["topic"]["name"] in ntopics, tstate + ["status":if(correct,"passed","failed")], tstate),
                    tstate,
                    state["topics"]
                )]
    , nstate
    )

action_retry (Use up one retry and visit the same topic again):
    [
        "label": translate("diagnostic.use retry"),
        "state": state + ["retries": state["retries"]-1],
        "next_question": random(topics[current_topic]["questions"])
    ]

action_stop (Stop the exam):
    [
        "label": translate("diagnostic.end test"),
        "state": state,
        "next_question": nothing
    ]

action_move_on (Move to the next topic, or end the exam if there are no more):
    let(
        state, after_answering,
        immediate_next_topics, topics[current_topic][if(correct, "leads_to", "depends_on")],
        unknown_topics, map(x["topic"],x,filter(x["status"]="unknown",x,state["topics"])),
        unknown_immediate_topics, filter(x["name"] in immediate_next_topics,x,unknown_topics),
        next_topics, if(len(unknown_immediate_topics), unknown_immediate_topics, unknown_topics),
        finished, len(next_topics)=0 or state["finished"],
        topic,
            if(not finished,
                next_topics[floor(len(next_topics)/2)]["name"]
            ,
                nothing
            ),
        [
            "label": translate("diagnostic.move to next topic"),
            "state": after_answering,
            "next_question": if(not finished, random(topics[topic]["questions"]), nothing)
        ]
    )

can_move_on:
    action_move_on["next_question"]<>nothing

next_actions (Actions to offer to the student when they ask to move on):
    let(
        feedback, retries_feedback+"\n\n"+translate("diagnostic.next step question")
    ,   [
            "feedback": feedback,
            "actions": if(not correct and state["retries"]>0, [action_retry], []) + if(can_move_on,[action_move_on],[action_stop])
        ]
    )

after_exam_ended (Update the state after the exam ends):
    let(
        state, after_answering,
        ntopics, map(t+["status": if(t["status"]="unknown","failed",t["status"])],t,state["topics"]),
        state+["finished": true]
    )

finished (Is the test finished? True if there are no unknown topics):
    len(unknown_topics)=0 or state["finished"]

total_progress:
    let(
        num_topics, len(state["topics"]),
        known, filter(tstate["status"]<>"unknown",tstate,state["topics"]),
        passed, filter(tstate["status"]="passed",tstate,known),
        num_known, len(known),
        num_passed, len(passed),
        [
            "name": translate("control.total"),
            "progress": if(num_topics>0,num_known/num_topics,0), 
            "credit": if(num_known>0,num_passed/num_topics,0)
        ]
    )

learning_objective_progress:
    map(
        let(
            tstates, map(state["topics"][j],j,topics_by_objective[lo["name"]]),
            known, filter(tstate["status"]<>"unknown",tstate,tstates),
            passed, filter(tstate["status"]="passed",tstate,known),
            num_topics, len(tstates),
            num_known, len(known),
            num_passed, len(passed),
            ["name": lo["name"], "progress": if(num_topics>0,num_known/num_topics,0), "credit": if(finished,num_passed/num_topics,if(num_known>0,num_passed/num_known,0))]
        ),
        lo,
        learning_objectives
    )

progress (Progress on each of the learning objectives, plus total progress):
    learning_objective_progress+
    total_progress

retries_feedback:
    translate("diagnostic.now assessing topic", ["current_topic": current_topic]) + " " +
    let(
        retries, state["retries"], 
        pluralise(retries, translate("diagnostic.one retry left"), translate("diagnostic.retries left", ["retries": retries ]))
    )
    + " " +
    let(
        p,total_progress["progress"],
        percentage, dpformat(100p, 0),
        translate("diagnostic.percentage completed", ["percentage": percentage])
    )

weak_objective_threshold (The amount of credit below which a learning objective is considered weak):
    0.6

finished_feedback:
    let(
        weak_objectives, filter(p["credit"]<weak_objective_threshold, p, learning_objective_progress),
    //
        translate("diagnostic.test is over") + " "
        +
        if(len(weak_objectives)=0,
            translate("diagnostic.passed all lo")
        ,
            translate("diagnostic.more work on lo", ["los": 
                join(map(lo["name"], lo, weak_objectives),", ")])
        )
    )

feedback:
    if(finished,
        finished_feedback,
        retries_feedback
    )

Custom diagnostic algorithm

Interface theme

Upload a new theme

Interface language

Allow students to print question content?

Show student's name?

Enter review mode immediately on ending the exam?

	Always	In submitted mode	Only in review mode	Never
Show answer correctness
Show the student's score
Show the maximum score
Show part feedback messages
Show expected answers
Show question advice

Pass threshold

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Skills Audit for Maths and Stats - Management and Creative and Cultural Industries (MGT6043)

Metadata

Taxonomy: mathcentre

Taxonomy: Kind of activity

Taxonomy: Context

Contributors

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History

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Checkpoint description

Will Morgan 8 months, 1 week ago

Will Morgan 9 months ago

Will Morgan 9 months ago

Will Morgan 9 months ago

Question groups

Topics

Learning objectives

Base diagnostic algorithm

Custom diagnostic algorithm

On leaving a question

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