Misconceptions of mathematics in higher education universities when learning with google classroom based on learning styles and gender differences

MISCONCEPTIONS OF MATHEMATICS IN HIGHER EDUCATION UNIVERSITIES WHEN LEARNING WITH GOOGLE CLASSROOM BASED ON LEARNING STYLES AND GENDER DIFFERENCES

Muhammad Turmuzi* , I Gusti Putu Suharta , I Wayan Puja Astawa ,
I Nengah Suparta

Doctoral Programme in Education Science Universitas Pendidikan Ganesha (Indonesia)

Received October 2023

Accepted December 2023

Abstract

The purpose of this study is to comprehensively describe the results of the analysis of the ability to understand concepts and misconceptions in terms of differences in learning styles, as well as gender differences. The data to be collected in this study is in the form of primary data and secondary data. The primary data is obtained from primary sources and secondary data is obtained from secondary sources. The primary source in this study is informants, namely students, and the secondary source is documentation of student test scores as an illustration of student abilities. The instruments in this study are student learning style tests and auxiliary instruments in the form of misconception diagnostic tests. Learning style test in the form of a questionnaire. This instrument is used to uncover visual, auditorial, and kinesthetic learning style variables. Misconception diagnostic tests use the Certainty of Response Index (CRI). The results of the CRI analysis based on student answer criteria are: (a) 5.83% of student problem solving do not know the concept (lucky guess); (c) 22.30% of the student problem solvers did not know the concept; (b) 50.18% of student problem solving mastered the concept well; and (d) 21.69% of student problem solving occurred misconceptions.

 

Keywords – Misconceptions, Google classroom, Learning styles, Gender.

To cite this article:

Turmuzi, M., Suharta, I.G.P., Astawa, I.W.P., & Suparta, I.N. (2024). Misconceptions of mathematics in higher education universities when learning with google classroom based on learning styles and gender differences. Journal of Technology and Science Education, 14(1), 200-223. https://doi.org/10.3926/jotse.2482

 

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    1. 1. Introduction

Technology has played a significant role in enhancing the contemporary education system across different levels of learning, including schools, colleges, and universities. Its utilization has not only improved the effectiveness of teaching and learning in classrooms by focusing on learners’ needs and outcomes but has also encouraged teachers to employ it as a tool to connect traditional methods with the modern educational demands, fostering overall learner development (Gray & Lewis, 2021). An examination of its implementation across various levels and settings reveals the swift adoption of diverse information and communication technologies, serving as catalysts to augment the learning process (Delić-Zimić & Gadžo, 2018; Gupta & Fisher, 2012; Nwobi, Ngozi, Rufina & Ogbonnaya, 2016; Tonui, Kerich & Koross, 2017; Ahmadi, Keshavarzi & Foroutan, 2011). Nevertheless, establishing student-centered learning environments in classrooms through technology is achievable. Google Classroom has made significant inroads in the education sector and is recognized as a powerful tool with vast educational potential. Developed by Google, Google Classroom is a free web service designed for schools, facilitating the seamless creation, distribution, and grading of assignments in a paperless manner. Its core objective is to simplify the sharing of files between teachers and students (Gupta & Pathania, 2021).

In a study conducted by (Basher, 2017) examining the impact of Google Classroom on the teaching efficiency of pre-teachers, an experimental approach was employed. The research sample was divided into two groups: a controlled group taught through traditional methods and an experimental group utilizing Google Classroom. The findings revealed significant statistical disparities in the results between the experimental and control groups when Google Classroom was utilized. College students’ teaching efficiency in planning, execution, and evaluation, as well as their academic achievements in computer‑related subjects, demonstrated improvement compared to the traditional teaching methods.

Heggart and Yoo (2018) conducted a study assessing the effectiveness of Google Classroom in engaging final-year primary teacher education students and promoting their active participation and autonomy. The research also aimed to analyze the potential impact of the platform on future teaching methods at the tertiary level. The findings indicated that Google Classroom enhanced student involvement, learning outcomes, and overall classroom dynamics. However, concerns regarding the speed of learning and user experience were identified. This data served as the basis for developing a framework to evaluate various online platforms, focusing on four key concepts: pace, accessibility, collaboration, and student involvement/autonomy (Naeem-Ahmed & ur Rehman, 2021; Borova, Chekhratova, Marchuk, Pohorielova & Zakharova, 2021; Bergström & Wiklund-Engblom, 2022). These concepts were instrumental in assessing the utility of other online learning platforms and informing pedagogical practices. Learning math through Google Classroom can provide many benefits, but several math misconceptions often occur when using the platform: Learning math often requires direct interaction with a teacher or fellow students. Lack of direct communication can make it difficult for students to understand complex mathematical concepts. Mathematical concepts are often easier to understand through visual demonstrations, such as diagrams or pictures. In online environments, the use of visual tools is often limited, making it difficult for students to understand the material well (Bringula, Reguyal, Tan & Ulfa, 2021; Radmehr & Goodchild, 2022).

Misconceptions of Mathematics According to Learning Style consist of Visual Learners: Students who are visual learners tend to have difficulty in mathematics if there is no visual representation of mathematical concepts. They need diagrams, graphs, or illustrations to understand mathematical formulas and relationships. Auditory Learners: Students who are auditory learners understand information through hearing. They may experience difficulties if mathematics lessons are primarily delivered through written text without oral explanation or discussion. Kinesthetic Learners: Kinesthetic type students require physical experience and direct interaction with the material. They may have difficulty understanding abstract math concepts without concrete examples or hands-on activities (Fleming, 1992). While math misconceptions according to gender differ, studies show that some female students tend to feel less confident in their math abilities, which can hinder their motivation and performance in this subject. Boys and girls may deal with math errors in different ways. Some research suggests that women tend to internalize their mistakes and find it more difficult to recover, while men may find it easier to overcome their mistakes and move on. Social interactions in mathematics classes, especially the gender stereotypes that develop, can influence student performance. For example, girls may feel held back by the perception that mathematics is a subject for boys (Hyde, Fennema & Lamon, 2010; Pajares, 2004).

Research on mathematical misconceptions based on learning styles needs to be carried out because mathematical misconceptions based on learning styles allow teachers to design learning experiences that suit students’ individual needs. Research has shown that many difficulties in learning math stem from students’ failure to understand the underlying concepts, which form the basis for the procedures they are using. Identifying and addressing these misconceptions is essential for promoting access and attainment for all students (Schnepper & Mccoy, 2017). Understanding how misconceptions are related to one another can be useful for identifying broader patterns that affect students’ long-term mathematical development (Rakes & Ronau, 2019). A personalized approach to learning can help students understand and overcome their mistakes more effectively. Research on learning style-based mathematical misconceptions helps teachers identify typical error patterns in students with certain types of learners. This allows the development of specific and effective interventions to help students overcome their errors and improve mathematical understanding. By understanding students’ learning styles and overcoming their misconceptions, teachers can help increase students’ motivation and confidence in learning mathematics. Students who feel understood and supported in their learning styles are more likely to be motivated to overcome their mistakes and achieve better academic progress (Alcaro, Carta & Panksepp, 2017; Felder & Silverman, 1988).

This research by Ecclestone, Hall, Coffield and Moseley (2004) and Kolb (1984) helps identify error patterns that are typical of certain types of learners. This allows teachers and education professionals to design specific and effective interventions to help students overcome their errors and improve mathematical understanding. Research into learning style-based mathematical errors helps develop teacher skills. By understanding how students of different types of learners deal with errors in understanding, teachers can design more effective and inclusive teaching strategies.

By understanding students’ learning types, teachers can allocate time and resources more efficiently. Focusing on aspects of understanding that require more attention can increase learning efficiency (Pashler, McDaniel, Rohrer & Bjork, 2008). Study by Hattie and Timperley (2007) provides the basis for the development of innovative, evidence-based mathematics teaching methods. By understanding how students of different types understand mathematics, teachers can design more engaging and effective learning experiences. The importance of research on mathematics misconceptions is linked to learning styles, creating learning environments that support and motivate students with various types of learners, increasing their understanding of mathematics, and overall, improving the quality of mathematics education. Visual, auditory, and kinesthetic learning styles have a significant influence on the learning process (Syofyan & Siwi, 2018). The learning style is a way to easily take in, process, retain, and apply the knowledge. Each learner has a unique manner of learning. The visual, aural, and kinesthetic learning styles of students can be distinguished. Students who learn visually learn through what they see, those who learn auditorially learn through what they hear, and those who learn kinesthetically learn through movement and touch.

Meanwhile, research on mathematical misconceptions based on gender has significant relevance and interest in the field of mathematics education because it can identify typical error patterns. Research by Else-Quest, Hyde and Linn (2010) helped identify patterns of math errors that are common among male and female students. Knowing these differences allows teachers to design more targeted teaching strategies, helping students overcome their mistakes effectively. Understanding the differences in errors between male and female students helps identify aspects of mathematics that may contribute to a lack of self-confidence in female students. By overcoming these mistakes, female students can build their confidence in understanding and mastering mathematics (Gunderson, Ramirez, Levine & Beilock, 2012). Research by (Eccles, 1994) provides teachers and educators with insight into the differences in male and female students’ mathematical understanding. With this knowledge, teachers can dig deeper into helping students overcome their mistakes, creating an inclusive and supportive classroom atmosphere.

From the description above, the objectives of this research are: (a). to identify the types of conceptual errors that are often experienced by students when they use Google Classroom as a mathematics learning platform. This identification helps in designing targeted interventions to improve student understanding. (b). analyze how students’ learning styles influence mathematical conceptual errors. By understanding how students with different types of learners tend to make conceptual errors in mathematics, this research can provide insight into how instruction can be tailored to individual learning needs. (c). to explore whether there are differences in mathematical conceptual errors between male and female students. Identifying these differences can help in developing more inclusive and supportive teaching strategies for students of both genders. (d). By combining analysis of mathematical misconceptions, learning styles, gender differences, and the use of Google Classroom, this research is expected to provide in-depth and useful insights for educational practitioners in improving the quality of mathematics teaching and learning.

From the research objectives above, the following Research Questions (RQ) can be formulated:

  • RQ (1): What is the learning style of students in learning with Google Classroom in elementary mathematics education courses? 

  • RQ (2): How is the ability to understand student concepts in learning with Google Classroom in elementary mathematics education courses when viewed from learning styles? 

  • RQ (3): How is the ability to understand student concepts in elementary mathematics education courses when viewed from gender and learning style? 

  • RQ (4): How are student misconceptions about learning with Google Classroom in elementary mathematics education courses when viewed from learning styles? 

  • RQ (5): What are the misconceptions of students in elementary mathematics education courses when viewed from gender and learning styles? 

2. Methodology

2.1. Types of Research

This study falls under the category of qualitative descriptive research (Roberts, Dowell & Nie, 2019; Seixas, Smith & Mitton, 2018; Soroush, Andaieshgar, Vahdat & Khatony, 2021), which aims to characterize student misconceptions in relation to the learning preferences of the students (Feldman, Cho, Ong, Gulwani, Popovic & Andersen, 2018; Jankvist & Niss, 2018; Mishra, 2020; Parwati & Suharta, 2020). Because the researcher only conducted analysis that reached the level of description–that is, methodically analyzed and presented the facts–it is referred to as descriptive research. In this study, all data–oral and written–from human sources that have been observed, as well as other relevant materials that represent the situation as it is, are analyzed and succinctly presented to address research questions using a qualitative methodology.

Conducting qualitative descriptive research can be an effective way to investigate and comprehend the features and attributes of a phenomenon (Rahman, 2016). Nevertheless, employing this study design has certain drawbacks, such as: Although it leads to richer understanding, qualitative descriptive research limits generalizability by yielding a significant amount of detailed information about a smaller number of participants. The conclusions won’t apply to different groups or situations. To ensure that the data obtained from qualitative research are accurate, careful planning and a lengthy process are necessary. It can take a few weeks or months to finish. Qualitative research relies less on outcomes and more on judgment and opinion. Qualitative data cannot be mathematically analyzed, and observations and conclusions might be influenced by one’s own knowledge and experience. Since qualitative research relies more on subjective interpretation than on objective measurement, it is not a good method for examining causation.

2.2. Research Sample

The research sample consisted of 38 third-semester students of the Primary School Teacher Education Study Program, at Mataram University, Indonesia who were taking the Primary School Mathematics Education course. They consisted of 7 male students and 31 female students. They carried out online lectures via Google Classroom for 9 meetings including carrying out a mathematics misconception test.

2.3. Data Collection Technique

The subjects of this research were 38 students at the University of Mataram Primary School Teacher Education Study Program Indonesia. The data that will be collected in this research will be primary data and secondary data. Primary data is obtained from primary sources and secondary data is obtained from secondary sources. The primary source in this research is informants, namely students, and the secondary source is grade documentation. In accordance with the form of a qualitative research approach and the data sources that will be used, data collection techniques in this research use questionnaires, documentation methods, and test methods. In qualitative research, the most common methods of data collection are interviews, focus groups, observations, and document study (Gill, Stewart, Treasure & Chadwick, 2008; Busetto, Wick & Gumbinger, 2020).

The learning style test is in the form of a questionnaire. According to Newton (1984), a questionnaire is a data collection technique that is carried out by giving respondents a set of questions or written statements to answer. The questionnaire used in this research is a closed questionnaire, namely a questionnaire that is equipped with answers so that students just have to choose the answer. This instrument is used to reveal visual, auditory, and kinesthetic learning style variables. Instrument scoring is made using a Likert scale with four alternative answers. The answers to each instrument have a gradation from very positive to very negative in the form of words, as shown in Table 1.

The questionnaire given to students is divided into 3 parts, namely: visual learning style questionnaire, auditory learning style, and kinesthetic learning style. The misconception diagnostic test is in the form of a test with elementary mathematics education material. Every time a student solves a problem, the lecturer always asks to write down a measure of the student’s level of confidence in the answer. This aims to diagnose the occurrence of misconceptions. The questionnaire of learning styles by students is carried out through google form. To create a Google Form for students to complete a learning style questionnaire, follow these steps: Open Google Forms by searching for “Google Forms” in your web browser and clicking on the first result. Click the +” button to create a new form. Give your form a title, such as “Learning Style Questionnaire.” Add questions to your form that assess different learning styles. You can use multiple‑choice, checkbox, or dropdown questions. In Figure 1 here is the front page of the google form.

Positive Statements

Score

Negative Statements

Score

Strongly Agree (SA)

4

Strongly Disagree (SD)

1

Agree (A)

3

Disagree (D)

2

Disagree (D)

2

Agree (A)

3

Strongly Disagree (SD)

1

Strongly Agree (SA)

4

Table 1. Questionnaire Statement Answer Score

 

Figure 1. Google Form front page view for learning style questionnaire

2.4. Research Instrument

Twenty statement indicators make up the mathematics misperception instrument used in this study. that claim was categorized into indicators based on the study variables, and its reliability and validity were then examined. The Table 2 and Table 3 below displays the results of the validity and reliability tests:

No

Question Indicator

r Count

r Table

Note

1

Find the meaning behind the notation and the concept of numbers.

0.56

0.26

Valid

2

Execute the integer operation.

0.45

0.26

Valid

3

Complete the integer operations problems in the story.

0.48

0.26

Valid

4

Solve story problems related to Least Common Multiple and Greatest Common Factor

0.69

0.26

Valid

5

Identify the circle’s area

0.33

0.26

Valid

6

Look at the characteristics of quadrilaterals.

0.56

0.26

Valid

7

Calculate a geometric shape’s surface area.

0.67

0.26

Valid

8

determining a tube’s volume

0.78

0.26

Valid

9

Solve whole-number word puzzles.

0.63

0.26

Valid

10

Apply your application knowledge to multiple-number and number factor issues.

0.57

0.26

Valid

11

Determine the outcomes of the addition and multiplication of prime numbers.

0.34

0.26

Valid

12

The block’s perimeter should be known.

0.7

0.26

Valid

13

calculating comparable comparison outcomes

0.48

0.26

Valid

14

Calculate the triangle’s surface area.

0.71

0.26

Valid

15

Determine a flat shape’s perimeter.

0.53

0.26

Valid

16

understand how prime numbers work and composite numbers

0.37

0.26

Valid

17

Calculate the angles at which two lines intersect and are parallel.

0.74

0.26

Valid

18

Find the location where the circle and triangle intersect.

0.55

0.26

Valid

19

determining algebraic fractional operations

0.48

0.26

Valid

20

Find the place value in a sequence of numbers.

0.38

0.26

Valid

Table 2. Validity test results

Validity testing is a crucial step in research to determine how well the measurement tool genuinely captures the idea or variable under investigation (Boateng, Neilands, Frongillo, Melgar-Quiñonez & Young, 2018; Franke & Sarstedt, 2019; Knekta, Runyon & Eddy, 2019). The degree to which an instrument can be trusted to measure what it is intended to measure is known as its validity. At the 5% significant level, all computed r values are greater than r table 0.26, according to the validity test findings in Table 2. Therefore, it can be said that every statement item in the mathematics misconceptions instrument has been deemed legitimate and is appropriate for use in the following study phase.

Table 3 shows that the reliability test findings yielded a reliability coefficient value (Cronbach Alpha) of 0.735 for all indicators, which is greater than 0.6. This indicates that the research instrument is dependable. The produced questionnaire instrument can be employed at the next stage of the study based on the findings of validity and reliability tests.

Reliability Coefficient (Cronbach Alpha) is a statistical measure used to measure the extent to which the items or questions in an instrument are consistent or reliable (Bujang, Omar & Baharum, 2018; Sivaprasad, Tschosik, Kapre, Varma, Bressler, Kimel et al., 2018; Taber, 2018). The Cronbach Alpha value ranges between 0 and 1, and the closer it is to 1, the higher the reliability of the instrument. This value of 0.735 shows a high level of instrument reliability. With a value greater than 0.6, the instrument is considered reliable. In this case, the value of 0.735 indicates that the instrument has a level of consistency and is reliable in measuring the variables or concepts studied.

Indicator Statement Number

Item Variance

Total Item Variance

Total Variance

Reliability

1

3.143

106.61

376.19

0.735

2

4.2524

3

3.3617

4

4.5196

5

6.3765

6

0.6734

7

4.7787

8

3.6923

9

5.9028

10

4.1835

11

2.2524

12

6.766

13

5.305

14

7.9406

15

2.5641

16

5.1471

17

6.9649

18

7.8435

19

6.6373

20

14.305

Table 3. Reliability test results with Cronbach Alpha

2.5. Data Analysis Techniques

This research is classified as qualitative descriptive research so that the data The existing ones are analyzed with qualitative data analysis techniques, namely the process of searching and systematically compile obtained data, field notes, and materials Other. Analysis of qualitative research data is carried out by organizing data, break it down into units, classify, organize into In the pattern, choose which ones are important and which ones will be studied so that you can a conclusion is made to convey to others. The data analysis technique used is narrative descriptive with using the Miles and Huberman model. Miles and Huberman (Rahman, 2016), suggest that activities in qualitative data analysis are carried out in a timely manner. interactive and takes place continuously until complete, so the data Saturated. A measure of data saturation is characterized by no more data or new information.

Qualitative data analysis in this study, namely: 1) data reduction is the stage of summarizing and focusing data from research analysis and eliminate unpatterned data, then the data is collected and selected in accordance with the purpose of the study; 2) Display data, data that has been reduced presented in the form of a short description so that it is easy to read and understand both in whole and in parts; and 3) conclusion drawing/ Verification, conclusions are drawn based on the results of the analysis of all data that has been obtained (Wong, 2008; St John & Johnson, 2000).

2.6. Research Procedure

Research procedure in this study are as follows.

  • Researchers conduct preliminary ability tests. The initial ability test (quis) is conducted online to find out Initial ability of students regarding subject prerequisite materials Elementary Mathematics Education.  

  • Researchers (lecturers) hold lectures. Lectures through the Google Classroom application based on the results of the analysis of the initial ability of college students. Some material should be given with portions that more because it is considered very important, but there are still many students who don’t understand yet. This resulted in pre-Elementary Mathematics Education material taking quite a long time (3x meetings), total meetings in Lectures have 9 meetings.  

  • Researchers (lecturers) conduct quizzes and assignments. At some meetings, researchers give quizzes and assignments online. These tasks are done on folio paper and archived on Google Each student’s classroom is a portfolio assessment. 

  • Researchers (lecturers) hold Midterm Exam. After the seventh meeting, lecturers held a Midterm Exam according to the faculty schedule. Students must add a level description certainty of the correctness of the answer to each question he solves. 

2.7. Misconception Analysis

Hasan, Bagayoko and Kelley (1999) identified the occurrence of misconceptions, at once can differentiate between not knowing the concept, by developing an identification method known as CRI (Certainty of Response Index). CRI measures the respondent’s level of confidence/certainty in answering each question (problem) given. A low CRI indicates a lack of confidence in the respondent’s concept in answering a question. On the other hand, a high CRI reflects high confidence and certainty in the concept in respondents (Firmasari & Nopriana, 2020; Latif, Mursalin, Buhungo & Odja, 2021). CRI was developed with a six scale (0 – 5) in Table 4 the following.

The following are provisions for distinguishing between knowing the concept, misconceptions and not knowing the concept for individual respondents. As in Table 5, those who can be categorized as having misconceptions are students who answer incorrectly, however, the CRI score they choose is in the high category (above 2.5). These misconceptions can come from students themselves, from lecturers who convey wrong concepts, inappropriate teaching methods, and wrong textbooks. Some misconceptions come from the students themselves, one of which is influenced by the students’ learning styles.

CRI

Criteria

0

(Totally guessed answer)

1

(Almost guess)

2

(Not Sure)

3

(Sure)

4

(Almost certain)

5

(Certain)

Table 4. CRI and its Criteria

Answer criteria

Low CRI (CRI < 2.5)

High CRI (CRI > 2.5)

Answer Correct

Correct answer but low CRI

lucky guess

Correct answer and high CRI

master the concept with Good

Wrong answer

Wrong answer and low CRI

don’t know the concept

Wrong answer but high CRI

misconceptions

Table 5. CRI analysis based on answer criteria

A learning style test in the form of a questionnaire is a data collection technique that is carried out by giving a set of questions or written statements to respondents to answer. The questionnaire used in this research is a closed questionnaire, namely a questionnaire that is equipped with answers so that students just have to choose the answer. To collect data on learning preferences based on gender and learning styles, a learning style test in the form of a questionnaire can be used. This questionnaire should be designed to assess students’ learning style preferences and provide a clear indication of their overall style preferences (Cohen, Oxford & Chi, 2006). This instrument is used to reveal visual, auditory, and kinesthetic learning style variables (Kamal, Karim, Kechik, Ni & Razak, 2021; Hussein-Ibrahim & Hussein, 2015).

The questionnaire given to students is divided into 3 parts, namely: visual learning style questionnaire, auditory learning style, and kinesthetic learning style. The diagnostic test for misconceptions is a test with material 1) Whole numbers, 2) Whole numbers, 3) Exponentiation and taking roots of whole numbers, 4) Multiples and factors of numbers, 5) Rational and irrational numbers, 6) Percents, comparisons and scales, 7) Build flat, and 8) Build. Every time a student solves a problem, the lecturer always asks to write down a measure of the student’s level of confidence in the answer. This aims to diagnose the occurrence of misconceptions. Filling out diagnostic test for misconceptions answers is carried out by students by answering questions through google form as seen in Figure 2. Sample questions for diagnostic test for misconceptions as seen in Figure 3.

 

Figure 2. Diagnostic test for misconceptions via google form

 

Figure 3. Sample questions for diagnostic test for misconceptions via google form

3. Results

The learning process is carried out online through Google Classroom, an online learning platform that allows teachers to create classes, share assignments, provide feedback, and interact with students online. (A’Yun, Suharso & Kantun, 2021). Following are the steps for conducting lectures using Google Classroom:

  • Step 1: Create a Class in Google Classroom.  

    Open Google Classroom: Visit Google Classroom and sign in using your Google account (or create an account if you don’t have one). Creating a New Class: Click the + button in the top right corner and select “Create class”. Fill in class information such as name, subject, and so on. 

  • Step 2: Adding Students to Classes 

    Invite Students: Click on the class you have created. At the top, click “Invite students” and enter the student’s email address or share the class code with them. 

  • Step 3: Create and Distribute Materials 

    Manufacturing Materials: On the class page, click “Assignments” or “Materials” to create new materials. You can upload files, create assignments, or add materials from Google Drive or YouTube. Distributing Materials: After creating materials, select the classes or students who will receive them. Set a deadline for submitting assignments if necessary. 

  • Step 4: Interact with Students 

    Providing Feedback: After students submit assignments, you can immediately provide feedback via Google Classroom. Interact via Comments: Below each assignment, there is a comments section where you and your students can interact, ask questions, or provide answers.