The Effects of Web 2.0 Technologies Usage in Programming Languages Lesson on the Academic Success, Interrogative Learning Skills and Attitudes of Students towards Programming Languages
Bibliographic record
Abstract
It is observed that teacher candidates receiving education in the department of Computer and Instructional Technologies Education are not able to gain enough experience and knowledge in “Programming Languages” lesson. The goal of this study is to analyse the effects of web 2.0 technologies usage in programming languages lesson on the academic success, interrogative learning skills and attitudes of students towards programming languages. “Pre-test-Post-test Control Group Quasi-Experimental Design Model” is used as research model in this study. Participants are divided into two groups named experimental group and control group. The work group chosen from the population for this study consists of 75 students in total receiving education in the 2nd grade of Computer and Instructional Technologies Teaching Department of Faculty of Ahmet Keleşoğlu, Necmettin Erbakan University in 2015-2016 academic year. “Academic Success Test” developed by researcher, “Attitude Towards Programming Languages Scale” which is adapted into Turkish by Durak (2013) and “Interrogative Skills Scale” developed by Aldan, Kandemir and Saraçoğlu (2013) are used in the study. As a result of the study, it is concluded that students receiving education within the experimental group are more successful. When analysing their attitudes towards programming languages, it is concluded that attitudes of students in experimental group are more positive than that of those in control group. Analysing the effects of students using and not using cooperative learning environment developed with Web 2.0 technologies on their interrogative learning skills, it is determined that post-test grades of experimental group are higher than those of control group. According to the result of the study, a learning environment designed with Web 2.0 technologies has high-level effects on students’ academic success and attitudes towards programming languages and has medium-level effect on their interrogative learning skills.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".