{"id":1666,"date":"2025-06-16T10:37:09","date_gmt":"2025-06-16T14:37:09","guid":{"rendered":"https:\/\/sites.temple.edu\/assessment\/?p=1666"},"modified":"2025-06-16T10:44:48","modified_gmt":"2025-06-16T14:44:48","slug":"dear-chatgpt-help-me-learn-to-make-a-pivot-table","status":"publish","type":"post","link":"https:\/\/sites.temple.edu\/assessment\/2025\/06\/16\/dear-chatgpt-help-me-learn-to-make-a-pivot-table\/","title":{"rendered":"Dear ChatGPT. Help me Learn to Make a Pivot Table"},"content":{"rendered":"\n

In my conversation last week with Karen Kohn<\/a>, we discussed the necessity of humans interacting with data to achieve the best analytical results. We were exploring statistics from EBSCO on the use of our e-books to understand the top subject areas within this platform.<\/p>\n\n\n\n

I shared with Karen the ChatCPT-generated analysis of usage trends and she described her alternative tool of choice – the pivot table function within Excel. Pivots are an easy way of summarizing data \u2013 in this case over 5,000 records. But Karen also noted that many library staff are not experienced with Excel.<\/p>\n\n\n\n

If the barrier to analysis is skill with Excel, is this something that ChatGPT could remedy? Yes, ChatGPT can help us to create learning materials. In this example, I asked ChatGPT to teach me how to create a pivot table for the same Excel file of e-book usage statistics (download here<\/a> for Temple Library staff). <\/p>\n\n\n\n

Here was my prompt: <\/p>\n\n\n\n

Using this excel file, create a step by step tutorial for a novice data librarian on the creation of a pivot table to show the top subject areas by total usage.<\/em> <\/p>\n\n\n\n

The result was pretty bare bones, but here are the first few steps (The full slide deck<\/a> is available here):  <\/p>\n\n\n\n

\ud83d\udcca Tutorial: Creating a Pivot Table for Top Subject Areas by Total Usage<\/strong> 
\ud83e\uddfe Step 1: Open Your Excel File<\/strong> 
Open the Excel file: 
eBookTitle_Jun042025_s8483726_20250301_20250531 (1).xlsx 
Identify the column labeled “BISAC”<\/strong> (subject area) and the column “Total Access”<\/strong> (usage). 
 
\ud83d\udccd Step 2: Select the Data Range<\/strong> 
Click on any cell in the dataset. 
Press Ctrl+A<\/strong> to select the entire dataset. 
Make sure there are column headers<\/strong> (e.g., \u201cTitle,\u201d \u201cBISAC,\u201d \u201cTotal Access\u201d). 
 
\u2795 Step 3: Insert a Pivot Table<\/strong> 
Go to the Insert<\/strong> tab on the top menu. 
Click on PivotTable<\/strong>. 
In the pop-up window: 
Ensure your full table range is selected. 
Choose to place the pivot table in a New Worksheet<\/strong>. 
Click OK<\/strong>. <\/td><\/tr><\/tbody><\/table>
Note:<\/strong> All formatting provided by ChatCPT<\/figcaption><\/figure>\n\n\n\n

For those who want to gain skills like this, we have many other tools (LinkedIn learning or other training are options). An advantage here is a more customized option, as ChatGPT’s tutorial utilized the file we specified<\/strong>.<\/p>\n\n\n\n

What are the other differences? How should this “novice data librarian” select the best tool for a small assessment task like analyzing usage statistics? I asked ChatGPT: <\/p>\n\n\n\n

\n

Provide me with a list comparing the use of ChatGPT to analyze this data and the use of Excel and pivot tables to perform that analysis<\/p>\n<\/blockquote>\n\n\n\n

ChatCPT came up with 14 examples. Here are a few:  <\/p>\n\n\n\n

\ud83d\udcca Comparison: ChatGPT vs. Excel PivotTables for Data Analysis<\/strong> <\/p>\n\n\n\n

Feature \/ Task<\/strong> <\/td>ChatGPT<\/strong> <\/td>Excel PivotTables<\/strong> <\/td><\/tr>
Ease of Use for Novices<\/strong> <\/td>High \u2013 conversational guidance, no need to know formulas <\/td>Moderate \u2013 intuitive GUI, but may require learning steps <\/td><\/tr>
Setup Required<\/strong> <\/td>Minimal \u2013 just upload file and ask questions <\/td>Requires opening Excel, selecting data, inserting PivotTable <\/td><\/tr>
Speed of Insight<\/strong> <\/td>Fast \u2013 instant aggregation, summaries, and recommendations <\/td>Fast \u2013 once PivotTable is set up, updates are automatic <\/td><\/tr>
Customization of Output<\/strong> <\/td>Very flexible \u2013 can sort, filter, summarize with plain text or charts <\/td>Moderate \u2013 can pivot, filter, and chart but with less automation <\/td><\/tr>
Text-Based Summaries<\/strong> <\/td>Strong \u2013 can explain patterns and generate narratives <\/td>Weak \u2013 user must interpret data visually or manually write summaries <\/td><\/tr>
Visualization Options<\/strong> <\/td>Can generate clean, customized charts <\/td>Good \u2013 built-in chart tools, but with limited customization <\/td><\/tr>
Error Checking \/ Guidance<\/strong> <\/td>Yes \u2013 explains problems (e.g., wrong column types) <\/td>Limited \u2013 errors are usually user-detected or implicit <\/td><\/tr><\/tbody><\/table>
Note:<\/strong> Table formatting provided by ChatCPT. No editing. <\/figcaption><\/figure>\n\n\n\n

It’s interesting that ChatGPT replicates my use of the word “novice” as the first difference. ChatGPT thinks very highly of itself. There’s nothing much it can’t do when compared side-by-side with Excel. It is indeed, easy to use, it’s fast, and no special software is required. ChatCPT is always willing to suggest patterns and recommendations. <\/p>\n\n\n\n

Unlike Excel, where the user must interpret the data, ChatCPT is happy to provide an answer. And this is where some skepticism and critical thinking – the essential human element – are required. <\/p>\n\n\n\n

Thanks again to Karen for prompting me to take my example one step further to share an additional use case for generative AI tools in support of assessment. <\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

In my conversation last week with Karen Kohn, we discussed the necessity of humans interacting with data to achieve the best analytical results. We were exploring statistics from EBSCO on the use of our e-books to understand the top subject … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":4680,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"jetpack_post_was_ever_published":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[52,54],"tags":[122,131,120,132],"class_list":["post-1666","post","type-post","status-publish","format-standard","hentry","category-process-improvement","category-technology-use","tag-ai","tag-artificial-intelligence","tag-data-analysis","tag-excel"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/posts\/1666","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/users\/4680"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/comments?post=1666"}],"version-history":[{"count":11,"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/posts\/1666\/revisions"}],"predecessor-version":[{"id":1677,"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/posts\/1666\/revisions\/1677"}],"wp:attachment":[{"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/media?parent=1666"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/categories?post=1666"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.temple.edu\/assessment\/wp-json\/wp\/v2\/tags?post=1666"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}