Attitude to the study of chemistry and its relationship with achievement in an introductory undergraduate course

Brown, Stephen; White, Sue; Sharma, Bibhya N.; Wakeling, Lara; Naiker, Mani; Chandra, Shaneel; Gopalan, Romila D.; Bilimoria, Veena

doi:10.14434/josotl.v15i2.13283

Cited by 18 publications

(27 citation statements)

References 18 publications

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“…The attitude to the subject does not always determine the grade that a student receives in a subject (Brown et al, 2015). The student's learning performance is affected not only by the interest in a subject but also by factors such as financial situation, scholarship, tuition fees, gender, adaptation ability to university conditions, desire to affirm oneself in a group, and the ability to use free time for personal purposes.…”

Section: Resultsmentioning

confidence: 99%

Using the Semantic Differential Method to Assess the Learning Motivation and Attitude of First-Year Students Towards Chemistry in University of EMERCOM of Russia

Svidzinskaya

Baskin

Mezentseva

2019

International Journal of Education and Practice

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It is an interesting to obtain an objective view of students' attitude toward a certain discipline and to construct effective pedagogical diagnostics based on the obtained data. Such data relates to both the academic performance of students and also their emotional attitude towards a subject and motivation to study it. This research study, using the semantic differential method, used a survey tool for measuring students' attitudes towards-Chemistry‖ as a body of knowledge and practical use. A questionnaire was administered to first-year students from the University of EMERCOM of Russia, who were studying Chemistry as a basic discipline. Methods of graphical and factor analysis were used to process the data obtained during the study. The concurrent results of both methods prove the applicability of the graphical data processing method for small subject groups' analysis, when factor analysis was not sufficiently accurate. As a result of statistical data processing, four basic factors, reflecting the students' attitude to the Chemistry course, were identified. The learners were seen having different attitudes depending on the major chosen, on gender and on the nature of the educational group. A lack of understanding of the role of Chemistry in professional disciplines was found to be a major cause for low learning motivation. It was also revealed that academic performance could be increased by monitoring the students' emotional states. A combination of both factor analysis and graphical data processing made this study both qualitative and quantitative. Contribution/Originality: The primary contribution of this research is to investigate the pedagogical diagnostics method of students' emotional perception in an educational process, based on the differential method, which allows tracking of the change in attitude towards a subject during training and choose teaching methods that increase motivation for learning.

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Section: Resultsmentioning

confidence: 99%

Using the Semantic Differential Method to Assess the Learning Motivation and Attitude of First-Year Students Towards Chemistry in University of EMERCOM of Russia

Svidzinskaya

Baskin

Mezentseva

2019

International Journal of Education and Practice

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“…Indeed, it is noted that abundance of studies have examined the effect of learning approaches (Al-Qahtani, 2015;Booth et al,1999;Brown, et al, 2015;Byrne, et al, 2001;Gappi, 2013;Hall et al, 2004;Lopez, et al, 2013;Mlambo, 2011;Varughese & Fehring, 2009) and teaching preference approaches (Booth et al, 1999;Brown et al, 2015;Hall et al, 2004;) on academic performance. However, evidences had also found inconclusive results on the effect of these factors on academic performance.…”

Section: Academic Performancementioning

confidence: 99%

“…Abundance of studies have examined various factors affecting academic performance, among others were learning styles (Albina, 2013;Bosnie & Smith, 2015;Boohan & Stevenson, 2014;Chermahini, Ghanbari & Talab, 2013;Tsingos, Wilkinson), learning approaches (Al-Qahtani, 2015;Brown, White, Wakeling & Naiker, 2015;Byrne, Flood, Willis, 2001;Booth, et al, 2013;Gappi, 2013; Lopez, Cervero, Suarez, Rodriquez, Pedro & Esteban, 2013;Mlambo, 2011;Hall, Ramsey & Raven, 2004;Sharon, 1999), teaching preference approaches (Hall et al, 2004;Booth, Luckett & Mlasenovic, 1999), socio economic status (Ali, Haider, Khan & Ahmad, 2013) and prior knowledge in subjects (Muller, Prinsloo & De Plesus, 2007). However, all of these studies were carried out in foreign countries while local study remains scarce.…”

Section: Introductionmentioning

confidence: 99%

Effect of Learning and Teaching Preference Approaches on Academic Performance among Malaysian Accounting Students

Zakaria¹,

Malek²

2019

ujer

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Academic performance is an important indicator of a student's achievement in accomplishing educational objectives specifically for learning and teaching. Many factors have been linked to academic performance; however, there are inconclusive results over as to whether individual factor contributes the most effect. In pursuit of this, the study aims to decipher students' academic performance in relation to learning and teaching preference approaches. 324 sets of a questionnaire were randomly sent to first year undergraduate accounting students. From this amount, 263 students responded, contributing to 81.2 percent rate of response. The study adapted the Approaches and Study Skills Inventory for Students (ASSIST) questionnaire that identify three learning approaches (deep, strategic and surface), two teaching preference approaches (deep and surface) and academic performance achievements. Data were analysed using Sequential Equation Modelling (SEM) with Smart Partial Least Square (PLS) version two. Results indicate that learning approaches had a significant effect on academic performance in which strategic had the most effect, followed by deep and surface. In contrast, teaching preference approaches did not have any effect on academic performance. An understanding of the magnitude of effect of learning and teaching preference approaches at the early phase would assist the students, educators and institutions to optimize the best approach and eventually expedite Malaysian education quality to be on par with other developed nations in pursuit of academic excellence.

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“…A certain part of the studies on academic achievement concerns the examination of the variables explaining the academic achievement of the student. Variables addressed in these studies include gender (Amro, Mundy & Kupczynski, 2015;Collins, McLeod & Kenway, 2000;Hajovsky & Kaufman, 2015;Pike, Schroeder & Berry, 1997;Scheiber, Reynolds, Mlambo, 2012); attitude (Brown, et al, 2015;Odom & Bell, 2015), anxiety (Khalaila, 2015;Macher, Paechter & Papousek, 2015), socio-economic level (Jurdak, 2014;Suphi & Yaratan, 2012), prior learning (Musso, Kyndt, Cascallar & Dochy, 2013;Power, Robertson & Baker, 1987;Strayhorn, 2006), self-efficacy (Valentine, DuBois & Cooper, 2004).…”

Section: Achievement and Factors Explaining Achievementmentioning

confidence: 99%

Predicting Achievement with Artificial Neural Networks: The Case of Anadolu University Open Education System

Aybek

Okur

2018

International Journal of Assessment Tools in Education

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This study aims to predict the final exam scores and pass/fail rates of the students taking the Basic Information Technologies -1 (BIL101U) course in 2014-2015 and 2015-2016 academic years in the Open Education System of Anadolu University, through Artificial Neural Networks (ANN). In this research, data about the demographics, educational background, BIL101U course mid-term, final and success scores of 626,478 students was collected and purged. Data of 195,584 students, obtained after this process was analysed through Multilayer Perception (MLP) and Radial Basis Function (RBF) models. Sixteen different networks attained through the combination of ANN parameters were used to predict the final exam scores and pass/fail rates of the students. As a result of the analyses, it was found out that networks established through MLPs make more exact predictions. In the prediction of the final exam scores, it was determined that there is a low level of correlation between the actual scores and predicted scores. In the analyses for the prediction of pass/fail rates of the students, networks established through MLPs ensured more exact prediction results. Moreover, it was determined that the variables as mid-term exam scores, university entrance scores and secondary school graduation year were of highest importance in explaining the final exam scores and pass/fail rates of the students. It was found out that in the higher institutions serving for Open and Distance Learning, pass/fail state of the students can be predicted through ANN under favour of variables of students which have been found as most the important predictors. ARTICLE HISTORY

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Attitude to the study of chemistry and its relationship with achievement in an introductory undergraduate course

Cited by 18 publications

References 18 publications

Using the Semantic Differential Method to Assess the Learning Motivation and Attitude of First-Year Students Towards Chemistry in University of EMERCOM of Russia

Using the Semantic Differential Method to Assess the Learning Motivation and Attitude of First-Year Students Towards Chemistry in University of EMERCOM of Russia

Effect of Learning and Teaching Preference Approaches on Academic Performance among Malaysian Accounting Students

Predicting Achievement with Artificial Neural Networks: The Case of Anadolu University Open Education System

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