Knowledge Surveys: An Effective and Robust Student Self-Assessment and Learning Tool

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Title: Knowledge Surveys: An Effective and Robust Student Self-Assessment and Learning Tool
Language: English
Authors: Joel Sloan, Timothy Frank, Lauren Scharff, Karin Becker
Source: Teaching & Learning Inquiry. 2025 13.
Availability: University of Calgary. Libraries & Cultural Resources, 410 University Court NW, Calgary, Alberta, T2N 1N4, Canada. Tel: 403-220-7175; e-mail: TLI@ucalgary.ca; Web site: https://journalhosting.ucalgary.ca/index.php/TLI/index
Peer Reviewed: Y
Page Count: 20
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Student Evaluation, Self Evaluation (Individuals), Knowledge Level, Learning Objectives, Engineering Education, Undergraduate Students, Civil Engineering, Majors (Students), Metacognition, Independent Study, Study Skills, Scores, Student Surveys, Educational Benefits, Program Effectiveness
ISSN: 2167-4779
2167-4787
Abstract: Knowledge surveys (KS) are a student self-assessment tool consisting of an ability statement for each learning objective in a course. Students respond by rating their confidence in performing a specified skill. Pre-unit KS transparently communicate learning objectives, alert faculty to self-assessed knowledge and skills students possess as they enter a unit of instruction and support metacognitive learning through the unit's lessons and activities. Postunit KS allow comparison of student self-assessed learning with faculty assessments of student learning, providing feedback to support continued development of self-assessment skills, as well as metacognitive and self-regulated learning skills. This study synthesizes several semesters of KS implementation across six different engineering courses, taught by six instructors utilizing multiple assessment types (exams, technical writing, design projects) and delivery modes (in person, hybrid, online). Results indicate that student KS scores aligned with faculty assessments, and alignment improved with cycles of performance and feedback in a subset of courses with similar assessment types. Student feedback indicates value in KS as a learning guide and as a helpful addition to these courses for learning and self-assessment, with student quotes demonstrating positive student learning practices and evidence of metacognitive thinking. Faculty find KS easily manageable; they also appreciate how KS help maximize alignment within their courses and provide additional data to support student learning. Knowledge surveys are thus an effective, robust, and relatively easy-to-implement method to systematically incorporate a self-assessment component into a variety of courses.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1490239
Database: ERIC
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  Data: Knowledge Surveys: An Effective and Robust Student Self-Assessment and Learning Tool
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  Data: University of Calgary. Libraries & Cultural Resources, 410 University Court NW, Calgary, Alberta, T2N 1N4, Canada. Tel: 403-220-7175; e-mail: TLI@ucalgary.ca; Web site: https://journalhosting.ucalgary.ca/index.php/TLI/index
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– Name: Abstract
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  Data: Knowledge surveys (KS) are a student self-assessment tool consisting of an ability statement for each learning objective in a course. Students respond by rating their confidence in performing a specified skill. Pre-unit KS transparently communicate learning objectives, alert faculty to self-assessed knowledge and skills students possess as they enter a unit of instruction and support metacognitive learning through the unit's lessons and activities. Postunit KS allow comparison of student self-assessed learning with faculty assessments of student learning, providing feedback to support continued development of self-assessment skills, as well as metacognitive and self-regulated learning skills. This study synthesizes several semesters of KS implementation across six different engineering courses, taught by six instructors utilizing multiple assessment types (exams, technical writing, design projects) and delivery modes (in person, hybrid, online). Results indicate that student KS scores aligned with faculty assessments, and alignment improved with cycles of performance and feedback in a subset of courses with similar assessment types. Student feedback indicates value in KS as a learning guide and as a helpful addition to these courses for learning and self-assessment, with student quotes demonstrating positive student learning practices and evidence of metacognitive thinking. Faculty find KS easily manageable; they also appreciate how KS help maximize alignment within their courses and provide additional data to support student learning. Knowledge surveys are thus an effective, robust, and relatively easy-to-implement method to systematically incorporate a self-assessment component into a variety of courses.
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
    Subjects:
      – SubjectFull: Student Evaluation
        Type: general
      – SubjectFull: Self Evaluation (Individuals)
        Type: general
      – SubjectFull: Knowledge Level
        Type: general
      – SubjectFull: Learning Objectives
        Type: general
      – SubjectFull: Engineering Education
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Civil Engineering
        Type: general
      – SubjectFull: Majors (Students)
        Type: general
      – SubjectFull: Metacognition
        Type: general
      – SubjectFull: Independent Study
        Type: general
      – SubjectFull: Study Skills
        Type: general
      – SubjectFull: Scores
        Type: general
      – SubjectFull: Student Surveys
        Type: general
      – SubjectFull: Educational Benefits
        Type: general
      – SubjectFull: Program Effectiveness
        Type: general
    Titles:
      – TitleFull: Knowledge Surveys: An Effective and Robust Student Self-Assessment and Learning Tool
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            NameFull: Joel Sloan
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            NameFull: Timothy Frank
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            NameFull: Lauren Scharff
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            NameFull: Karin Becker
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              Y: 2025
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