Academic Insights: Evaluating Student Scores Across Subjects  

Open Dataset 👉👉👉 Result Dataset

✅ Step 1: Load Dataset into Power BI

1. Open Power BI Desktop

2. Go to Home > Get Data > Excel (or appropriate format)

3. Select your file containing the dataset

4. In the Navigator pane:

   • Select the sheet or table (e.g., Sheet1)

   • Click “Transform Data” to open Power Query Editor

5. Rename the query to result in the Query Settings pane (if not already named)

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🔧 Step 2: Transformation Steps in Power Query (for result)

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🛠️ Step 2.1: Ensure Correct Column Headers

Ensure you have the following columns:

• Student ID, Name, Math, Science, English, History, Computer Science

If not:

• Use "Use First Row as Headers"

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🛠️ Step 2.2: Set Proper Data Types

1. Set data types:

   • Student ID → Whole Number or Text

   • Name → Text

   • Subject columns → Decimal Number

Do this by selecting the column → right-click → Change Type

✅ 1. New Column using Power Query (M Query)

To add a Total Score column:

= Table.AddColumn(Source, "Total Score", each [Math] + [Science] + [English] + [History] + [Computer Science])

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✅ 2. Only One DAX Measure

To calculate the Average Score per Student (out of 5 subjects):

AverageScore = [Total Score] / 5

Use this in a card or table visual.

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✅ 3. Step-by-Step Power BI Report Design – Research Questions

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🔍 Q1: Who are the top-performing students?

Steps:

1. Use the Total Score column from M query.

2. Create a Table visual with:

   • Name, Total Score, AverageScore

3. Sort by Total Score descending.

4. Apply a Top N filter (e.g., Top 5).

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🔍 Q2: What is the performance trend across subjects?

Steps:

1. In Power Query, Unpivot subject columns:

   Table.UnpivotOtherColumns(Source, {"Student ID", "Name"}, "Subject", "Score")
   

2. Create a Column Chart:

   • Axis: Subject

   • Value: Average of Score

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🔍 Q3: Who needs improvement? (Low scorers)

Steps:

1. Use the AverageScore DAX.

2. Create a Table visual with:

   • Name, AverageScore

3. Add a filter: AverageScore < 40 or conditional formatting to highlight.

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🔍 Q4: Subject-wise comparison for each student

Steps:

1. Use Clustered Bar Chart or Radar Chart (with custom visuals).

2. Axis: Subject

3. Values: Score

4. Legend or slicer: Name

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🔍 Q5: Distribution of scores across students

Steps:

1. Use a Histogram or Box Plot (custom visual).

2. Use the Total Score or AverageScore.

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