vicharanashala-org/Internship_Support_Query_Resolution_Dataset

Internship Support-Query Resolution Dataset

Support-query records from an internship programme's helpdesk: how each query was raised, which theme it's about, and how it was resolved. 4,093 rows, one row per query. No free-text query content is included — every row's content is represented only by query_theme_code.

Files

File	Rows	Contents
internship_query_resolution_export_2026-07-08_anonymized.csv	4093	one row per query, 16 columns
query_theme_distribution.csv	16	query_theme_code, query_theme_label, description, count, pct — what each query was about
resolution_pathway_distribution.csv	6	resolution_pathway_code, resolution_pathway_label, description, count, pct — how each query got resolved
LICENSE	—	MIT License

The two lookup files are separate because a query's topic and how it got resolved are independent facts about it.

Columns

#	Column	Meaning
1	query_id	opaque internal identifier, unique per row
2	raised_by_student_id	pseudonymous asker id (STUDENT_00001…), consistent across rows for the same person
3	raised_at	ISO 8601 timestamp the query was raised
4	query_theme_code	what the query is about — look up the name in query_theme_distribution.csv
5	status	coarse resolution outcome: human_resolved, resolved, resolved_by_asker, resolved_by_AI_agent, super_escalated
6	resolution_pathway_code	how the query was resolved — look up the name in resolution_pathway_distribution.csv
7	resolution_pathway_label	the resolution-pathway name, inlined here for convenience
8	trigger_source	how the query entered the system: header_link, legacy_hashtag, peer_raise, AI_agent_proactive, or NA
9	resolution_type_raw	whether an FAQ action was taken while resolving: find_faq, create_faq, custom_reply, or NA
10	linked_faq_id	which of 114 existing FAQ entries was reused (<topic>.<item>, e.g. 4.3), or NA
11	linked_faq_id_source	which underlying system field that FAQ link was read from, or NA
12	responded_at	ISO 8601 timestamp of the response, or NA
13	resolver_type	the system's resolver category, 10 values — see Resolution pathway below for how this relates to resolution_pathway_code
14	resolver_student_id	pseudonymous resolver id, same scheme as column 2, or NA — populated only when an individual person (not a script or the asker) resolved the query
15	escalation_trigger	too_many_skips, too_many_ambiguous, or NA
16	accepted_at	ISO 8601 timestamp of acceptance, or NA

All blank cells are written as the literal string NA.

Query theme

What the query is about: one of 16 categories (T1–T16), assigned by reading each query's actual text against the category definitions below — not derived from any other column. Names, full descriptions, counts, and percentages are in query_theme_distribution.csv.

Resolution pathway

How the query was resolved, independent of its topic. status alone is too coarse to distinguish resolution pathways — for example, every peer-resolved query (286 of them) carries the same status = human_resolved value as an administrator-resolved query. resolution_pathway_code makes this distinction explicit, grouping the system's 10 raw resolver_type values into 6 categories:

Code	Label	resolver_type values included
P1	Admin bulk/script-resolved	bulk_admin_script, bulk_admin_script_legacy
P2	Admin individually-resolved	human_admin_direct, human_admin_direct_legacy, human_resolved_unattributed
P3	Peer-resolved	peer_answered_no_formal_approval_record, peer_genuine_approve
P4	AI-agent resolved	AI_agent
P5	Self-resolved	self_resolved_by_asker
P6	Unresolved/other	unresolved_or_other

resolver_type is kept alongside resolution_pathway_code because it is more granular — it is the only column that distinguishes a legacy-era action from a current-era one (e.g. bulk_admin_script vs. bulk_admin_script_legacy, both grouped under P1).

Column notes

resolver_student_id supports resolver-effort analysis (who resolves the most queries). Its population does not follow resolution_pathway_code cleanly: within P1 (bulk-script), 570 of 2,000 rows carry an individual id even though bulk-script resolution sounds automatic; within P2 (direct admin), only 45 of 1,157 rows do — most "direct" admin resolutions in this data are not attributed to a specific person. This is a property of the source system, not an artifact of this release.

escalation_trigger is set by the system's own internal logic (too many skipped attempts, too much ambiguity) before a resolution pathway is settled, independent of resolution_pathway_code. In this data it only co-occurs with P1, P2, or P5 — P3 (peer) and P4 (AI-agent) rows never carry this flag.

License

MIT License, see LICENSE.