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Comparing instructor-led, video-model, and no-instruction control tutorials for creating single-subject graphs in

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Instructor-led and video-model tutorials effectively teach graphing for single-subject designs, outperforming written instructions for master's students. Training improved performance on trained and untrained graph types.

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Area of Science:

  • Behavior Analysis
  • Educational Psychology
  • Data Visualization

Background:

  • Visual inspection of single-subject data is crucial for behavior analysts.
  • A lack of consensus exists regarding optimal methods for teaching graph construction in single-subject designs.
  • Previous research by Tyner and Fienup (2015) provides a foundation for this study.

Purpose of the Study:

  • To compare the effectiveness of instructor-led, video-model, and no-instruction control tutorials on graphing performance.
  • To extend previous findings on teaching graph construction for single-subject designs.
  • To evaluate the impact of different instructional methods on master's students' graphing skills.

Main Methods:

  • A systematic replication and extension using a repeated-measures between-subjects design.
  • 81 master's students with prior Microsoft Excel experience participated.
  • Comparison of three tutorial conditions: instructor-led, video-model, and a control group.

Main Results:

  • A statistically significant main effect was found for pretest, tutorial, and posttest submissions across all groups.
  • The main effect of the tutorial group itself was not statistically significant.
  • A significant interaction between tutorial group and submissions indicated instructor-led and video-model tutorials were superior to the control condition.
  • Training positively impacted performance on both trained and untrained graph types (multielement graphs).

Conclusions:

  • Both instructor-led and video-model tutorials are more effective than written conventions for teaching graph construction to graduate students.
  • Instructional methods significantly influence graphing performance in single-subject designs.
  • Training generalizes to untrained graph types, suggesting robust skill acquisition.