Associations between teachers’ interpersonal behaviour, classroom learning environment and students’ outcomes

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I investigated associations between teachers’ interpersonal behavior, the classroom learning environment and students’ outcomes. The Questionnaire on Teacher Interaction (QTI), What Is Happening In this Class? (WIHIC), and Test Of Science-Related Attitudes (TOSRA) were used with a sample of 785 students from 75 classes in five high schools in New York. Results from the New York State Regents examination taken in June were collected for 603 students in 37 classes as a measure of achievement. Data analyses supported the factor structure, internal consistency reliability and discriminant validity of the WIHIC questionnaire and the attitude scales from TOSRA, as well as WIHIC scales’ ability to differentiate between classrooms. Data analyses also supported the internal consistency reliability of the QTI and its ability to differentiate between classrooms. Also, the circumplex nature of the QTI was supported by analyzing its pattern of scale intercorrelations. Overall, the learning environment instruments (QTI and WIHIC) and attitude instrument (TOSRA) were found to be valid and reliable when used with high school science students in New York. Simple correlation and multiple regression analyses revealed positive associations between the learning environment and students’ attitudes. All seven WIHIC scales were statistically significantly correlated with attitudes to science. Overall Teacher Support was the strongest independent predictor of student attitudes to science. Positive but weak associations were also found between learning environment and achievement (especially Task Orientation, Equity, Student Cohesiveness and Involvement). Also Equity was positively and independently associated with achievement. Associations were found between teachers’ interpersonal behavior and attitudes (Adoption of Scientific Attitudes and Enjoyment of Science Lessons) and achievement. With the student as the unit of analysis, the Adoption of Scientific Attitudes scale was significantly correlated with all the QTI scales except Strict. With the class as the unit of analysis, all the QTI scales were significantly correlated with Adoption. Leadership and Understanding were the only independent predictors of Adoption. Leadership, Understanding, Helping/Friendly, Uncertain, and Dissatisfied scales were positively and independently associated with Enjoyment of Science Lessons with the student as unit of analysis whereas, with class as unit of analysis, only Uncertain was positively and independently associated with Enjoyment. Associations were mostly in the expected directions, but with a few exceptions (e.g. Uncertain behavior was negatively related to student achievement). Commonality analyses were undertaken to investigate the unique and common contributions of the WIHIC and the QTI scales to the variance in student outcomes. The benefit of using both instruments together to predict Enjoyment, but not Adoption, was supported by the findings. Therefore, it is worthwhile to include both the WIHIC and QTI in the same study of students’ enjoyment of science. For achievement, neither the WIHIC nor the QTI accounted for much unique or common variance. A subsample of 40 students was interviewed using questions pertaining to each scale of the QTI, WIHIC and TOSRA in order to check the construct validity of the questionnaires. Findings from these interviews reinforced the validity of the WIHIC, QTI and TOSRA for use with the sample of high school biology students in New York because interview findings were mostly consistent with the means obtained for each scale. By providing validation data for the WIHIC, QTI and TOSRA, this study has provided New York teachers with instruments that can easily be used to assess associations between learning environment, teachers’ interpersonal behavior and student outcomes. Also, this research has practical implications that suggest that teachers wishing to improve their students’ attitudes and achievements should place greater emphasis on Leadership, Helping/Friendly, Understanding, and Student Responsibility/Freedom in their classroom. Also Student Cohesiveness, Teacher Support, Involvement, Cooperation and Equity should be emphasized.

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... It was revealed that those who had high academic self-efficacy also had higher grade point averages (GPAs). Studies in the realm of learning environments began with the endeavors of Lewin and Murray in the 1930s and advanced through with Walberg and Moos in the 1960s who paved the way for additional research all over the world (Madu, 2010). Dorman (2001) employed 1055 mathematics secondary school students in Australia and asked them to complete two instruments (What Is Happening In This Classroom? ...
... The scales are Student Cohesiveness, Teacher Support, Involvement, Investigation, Task Orientation, Cooperation, and Equity that help to measure classroom learning environment. This instrument was developed by Fraser, Fisher, andMcRobbie (1996, as cited in Madu, 2010) and has been used and cross-validated in different researches including Aldridge, Fraser, and Huang (1999), Dorman (2003), Margianti, Fraser, and Aldridge (2004), Chionh and Fraser (2009) among others (cited in Madu, 2010). Therefore, the validity and reliability of this scale have been confirmed for investigating students' perceptions toward their learning environment. ...
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The present study sought to examine the relationship between classroom environment and English as a Foreign Language (EFL) learners' academic self-efficacy. To this end, a sample of 200 advanced EFL learners (146 females and 54 males) completed the What is Happening In This Class? (WIHIC) which consists of seven scales including Student Cohesiveness, Teacher Support, Involvement, Investigation, Task Orientation, Cooperation, and Equity that help to measure classroom learning environment. The Self-Efficacy for Learning Form (SELF-A) was also administered to gauge the participants’ academic self-efficacy. In order to analyze the data, Spearman rank-order correlation was run. The results revealed that there was a significant relationship between EFL learners’ classroom environment and their self-efficacy (rho = .438). The findings reflected that the highest relationship was between task orientation and self-efficacy (rho = .433) followed by the relationship between student cohesiveness and self-efficacy (rho = .353). However, the lowest relationship was found for the relationship between cooperation and self-efficacy (rho = .199). Overall, the results highlight the relationship between classroom environment and academic self-efficacy.
... There are several instruments used in prior research to assess perceptions of classroom learning environment. The implication from prior research is that students' outcomes might be related to perceptions of classroom learning environment; in other words, students' achievement might be improved by creating better classroom learning environments (Fraser, 1986;Madu, 2010;Tas, 2016). Also, with integrating technology into education, growing number of researchers have focused on smart classroom learning environment and new forms of classroom learning environment like flipped classroom learning environment in recent years (Butzler, 2014;Jena, 2013). ...
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In the perspective of lifelong learning, lifewide learning and learning society, learning environments have expanded from schools to a more broader space, and even to the whole city. School, family, community, workplace, and museum can be regarded as typical learning environments in a smart city. But few research about learning environments had been found on the combination of schools, families, communities and other learning situations. The purpose of this paper is to describe and analyze the characteristics of typical learning environments in smart cities, as well as the relationship of these learning environments. A mixed survey was carried out, a secondary analysis of statistical data of 68 cities was conducted, and a telephone survey with a sample of 13,600 people in 68 cities was used for data collection. It was found that there were significant differences in the development levels of five typical learning environments in smart cities, i.e., school, family, community, workplace, and museum learning environments. Some relations among the five typical learning environments were found.School had high relationship with community and museum learning environments. Family was strongly correlated with workplace and museum learning environments. Community was associated with museum, family, and school learning environments, but no significant relation existed between participation in community activities and workplace learning. As a public learning space, museum was related to all other learning environments. Further research should be taken to explore the reasons behind these correlations and their influencing factors.
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Conference Paper
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