Collaborative Behavior Patterns of Students in Programming Instruction

2022 ◽  
pp. 073563312110622
Author(s):  
Sinan Hopcan ◽  
Elif Polat ◽  
Ebru Albayrak
Keyword(s):  
Preservice Teachers ◽  
Programming Languages ◽  
Sequential Analysis ◽  
Pair Programming ◽  
Programming Approach ◽  
Behavioral Learning ◽  
Programming Instruction ◽  
Collaborative Behavior ◽  
Lag Sequential Analysis ◽  
Programming Process

The pair programming approach is used to overcome the difficulties of the programming process in education environments. In this study, the interaction sequences during the paired programming of preservice teachers was investigated. Lag sequential analysis were used to explore students’ behavioral patterns in pair programming. The participants of the study consist of 14 students, seven pairs enrolled in a Programming Languages course. The findings indicate that there are significant behavioral learning sequences. During the program development process, students hesitated to create an algorithm and to improve an existing one while proposing the next step. In addition, they constantly waited for approval. Collaborative behaviors such as giving and receiving feedback and helping other partners were less observed in females. In addition, significant sequential driver and navigator behaviors were presented. The findings of the study have important implications for instructors and designers when using a pair programming approach in teaching programming. In the future, programming instruction environments can be designed by considering the learner behaviors that are presented in this study.

Competitive and Cooperative Decision-Making Groups

1980 ◽  
Vol 7 (4) ◽  
pp. 457-478 ◽  
Author(s):  
Dorothy Lenk Krueger
Keyword(s):  
Decision Making ◽  
Sequential Analysis ◽  
Group Interaction ◽  
Communication Patterns ◽  
Sample Group ◽  
Lag Sequential Analysis ◽  
Patterns Of Communication ◽  
Two Measures ◽  
Cooperative Decision Making

This study investigates differences among four decision-making groups and describes the patterns of communication unique to two groups. In the first part of the investigation, four decision-making groups are given either competitive or cooperative inducements and are compared on two measures: competition and satisfaction. The two groups given the competitive inducement (Groups I and III) were found to have significantly higher competition and lower satisfaction than the groups given cooperative inducements (Groups II and IV). In the second part of the study a lag sequential analysis is conducted on the coded communicative sequences in the highest and lowest competition groups (I and II, respectively). This analysis yields patterns to decision-making unique to each sample group. Group I's communication is characterized by highly probable (above-chance) sequences of disagreement messages and few probable agreement messages. Group II's communication patterns consist of highly probable sequences of decision development and probable agreement/support messages throughout the group interaction.

Autonomously Generative CMM Part Programming for In-Process Inspection

2003 ◽  
Vol 125 (1) ◽  
pp. 105-112 ◽  
Author(s):  
Jiancheng Liu ◽  
Hung Ng ◽  
Kazuo Yamazaki ◽  
Kazuo Nakanishi
Keyword(s):  
Coordinate Measuring Machine ◽  
Programming Approach ◽  
Programming System ◽  
Programming Methodology ◽  
Process Measurement ◽  
Complex Part ◽  
Measuring Machine ◽  
Programming Process ◽  
Machining Operations ◽  
Part Model

This paper presents an in-process inspection-oriented autonomously generative CMM (Coordinate Measuring Machine) part programming methodology with a simplified part programming process to decrease the time and skill for CMM part programming. The proposed method differs from the CAD-based CMM programming approach. Using this new method, machining features in a complex part can be efficiently inspected after any stage of machining operations or after machining these features. The concept of the proposed CMM part programming approach and its implementation are described in this paper. The results obtained from the feasibility study show the developed CMM part programming system can autonomously generate an inspection plan for a work-in-progress part model generated from NC programs to realize the in-process measurement.

Patient cues and medical interviewing in general practice: examples of the application of sequential analysis

2003 ◽  
Vol 12 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Christa Zimmermann ◽  
Lidia Del Piccolo ◽  
Maria Angela Mazzi
Keyword(s):  
Primary Care ◽  
Pattern Recognition ◽  
General Practice ◽  
Communication Skills ◽  
Sequential Analysis ◽  
Formal Training ◽  
Medical Interview ◽  
Medical Interviewing ◽  
Lag Sequential Analysis ◽  
Pattern Recognition Analysis

SUMMARYAims – To illustrate how sequence analysis may be applied to the medical interview to: 1. explore how physicians without formal training in communication skills elicit and respond to patient cues and expression of expectations and opinions; and 2. test the hypothesis that physicians' closed ended questions determine the use of subsequent closed ended questions. Methods – 238 consultations in primary care, coded with the Verona Medical Interview Classification System, were analysed. Lag 1 analysis was applied to study which physician behaviour precedes and follows patient cues. Pattern recognition analysis for five lag sequences was performed to test the occurrence of predefined specific code chains, where a closed and an open ended question were followed either by two closed-ended questions or by two patient facilitating interventions Results – Patients' cue offers were most likely after facilitative interventions, but not after open-ended questions; physicians were most likely to respond to these expressions with facilitation. Physicians' tendency to use closed ended questions increased after previous closed questions and decreased after an open-ended question. Conclusions – Lag sequential analysis and pattern recognition analysis are useful methods to study exploratory and theory driven hypotheses and allow an initial approach to validate the supposed appropriateness of specific physician interventions.Declaration of Interest: none.

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