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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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Undergraduate Students' Critical Online Reasoning-Process Mining Analysis.

Susanne Schmidt1, Olga Zlatkin-Troitschanskaia1, Jochen Roeper2

  • 1Department of Business and Economics Education, Johannes Gutenberg University, Mainz, Germany.

Frontiers in Psychology
|January 11, 2021
PubMed
Summary
This summary is machine-generated.

Students demonstrate critical online reasoning (COR) through strategic or avoidance information processing. Strategic processors are more efficient and achieve higher task performance, while avoidance processors show poorer performance.

Keywords:
Critical Online Reasoning Assessmentevent logseye-trackinglatent class analysisonline information processingperson-oriented approachprocess miningrepsonse process patterns

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Area of Science:

  • Educational Psychology
  • Human-Computer Interaction
  • Information Science

Background:

  • Effective online learning requires students to critically search, evaluate, and verify online information.
  • This skill, termed critical online reasoning (COR), is crucial for academic success in the digital age.

Purpose of the Study:

  • To operationalize and assess critical online reasoning (COR) in undergraduate students.
  • To distinguish between different COR profiles using both task and process performance data.
  • To investigate the relationship between information processing patterns and learning outcomes.

Main Methods:

  • Developed the Critical Online Reasoning Assessment (CORA) to measure student-level performance.
  • Utilized event log data (gaze durations, fixations) for online information processing level analysis.
  • Employed process mining (PM) and latent class analysis (LCA) for a person-oriented approach.

Main Results:

  • Identified two distinct student profiles: 'strategic information processers' (11 students) and 'avoidance information processers' (21 students).
  • Strategic processors exhibited more comprehensive and efficient information processing, correlating with higher task performance.
  • Avoidance processors demonstrated poorer process performance and lower task scores.

Conclusions:

  • Empirically confirmed two COR profiles (high vs. low performance) using combined task and process data.
  • High-performing students are significantly more efficient in strategic information processing.
  • Further qualitative research is needed to explore visual attention dynamics within these COR profiles.