Is the self-efficacy of maths teachers related to teaching competency?

Is the self-efficacy of maths teachers related to teaching competency?

The role of teachers is one of the essential elements that ensure the proper functioning of the education system and the world for students’ benefit.  In addition to guiding them academically, teachers can influence children’s future, making them better human beings. A teacher can instill content knowledge, life skills, good dispositions, traditional values, and modern-day issues to students.

Teaching mathematics goes beyond the knowledge capacity of teachers and pre-service teachers. In other words, equipping students with different 21st-century skills and attitudes is the main goal of teaching mathematics, rather than transferring content knowledge. The confidence teachers have in their planning and implementation skills affects their teaching and learning objectives in online education. A number of problems can arise in the classroom if the teacher is lacking in confidence. A teacher may have comprehensive mathematical knowledge and skills yet have low self-confidence while lecturing. They may not be able to use their expertise and abilities adequately in the classroom teaching process, leading them to perform their profession poorly. The self-confidence of the teacher is important in terms of providing more effective teaching to their students.

 What is the meaning of maths self-efficacy?

As defined by Bandura (1997), mathematics self-efficacy is one’s beliefs or perceptions concerning their abilities in mathematics education. Mathematics self-efficacy is operationalized as a belief which should be internalized by teachers and pre-service teachers. On the other hand, teaching competencies can be defined as the knowledge and skills that they must perform in their profession effectively and efficiently. Without sufficient knowledge, enthusiasm, and self-efficacy in these areas, it is unlikely that future elementary teachers will be able to provide effective instruction (Battista 1986; Stevens & Wenner, 1996; Tosun, 2000).

Mathematics self-efficacy is different from teachers’ mathematics competencies. Teacher competencies refer to a teacher’s professional knowledge and expertise, while teacher self-efficacy is tied to a more general concept. Teacher self-efficacy is more than having technical experience and skills; it also includes confidence that one has in putting this knowledge and competencies into practice. Having this confidence helps to provide an effective teaching environment in the classroom and to manage the negativities that may be encountered in classroom management by strengthening the student-teacher relationship. Gavora (2010) pointed out that a teacher’s high self-efficacy enables them to use their professional knowledge and skills successfully. Students learn more from teachers who have high self-efficacy (Zuya et al., 2016).

In line with Küçükalioğlu and Tuluk (2021), mathematics teachers with high self-efficacy were observed to have a positive effect on students’ mathematical achievement. Therefore, the self-efficacy of mathematics teachers seems to be the determining factor in their way of teaching and behaviour in class. According to Bandura (1995), teachers with low self-efficacy tend to create an environment that has an adverse effect on students’ mathematical achievement. I would add that if a teacher does not attend their lesson prepared for the misconceptions about the related content that students may encounter, they may not notice the student’s current misconception, which may lead to the student’s learning based on faulty thinking and understanding.

The association between mathematics education, self-efficacy, and teaching competency

The question of how the mathematics competencies and self-efficacy of teacher candidates who grew up with technological advancements (i.e. the flipped learning approach) have been a matter of curiosity. What are the teaching competencies and self-efficacy of elementary mathematics pre-service teachers in teacher education at a foundation university?

When we look at the studies carried out to date in general, we can say that most of the studies (e.g., Çakıroğlu & Işıksal (2009); Gülten (2013)) examining the variables focused on gender, age, and grade level were conducted on pre-service teachers and teachers as study groups. Reviewing the previous studies, we observed that most of them were carried out in state universities, and that teacher education programs involved preservice mathematics teachers who were exposed to insufficient practicum. Having analyzed the literature, there was no research carried out on pre-service teachers who have been educated in a foundation university in Istanbul!

 Considering that practicum courses attended by freshmen years were intensively included in the internship in order to improve pre-service teachers’ mathematics self-efficacy and mathematics teaching competencies, examining the relationship between mathematics self-efficacy and mathematics teaching competencies aims to bring a different perspective to the related literature.

Our research into self-efficacy and mathematics

We conducted a study with second, third, and fourth-grade teacher candidates at the department of Middle School Mathematics Teaching at MEF University in Istanbul, Turkey, in the 2021-2022 academic year. When we analyze the scores obtained through the questionnaires (Özgen & Bindak, 2008 for self-efficacy;  Esendemir et al., 2015 for teaching competency), we can say that the self-efficacy of pre-service mathematics teachers is higher than their competence in teaching mathematics. There is a relationship between pre-service mathematics teachers’ mathematics self-efficacy and mathematics teaching competency. The results revealed that there is a statistically significant and positive relationship between the pre-service mathematics teachers’ self-efficacy and their teaching competencies. This result means that as mathematics teacher candidates’ teaching competencies increase, their self-efficacy also increases (Check for the full manuscript of the graduation thesis).

Conclusion

We mentioned that instructors have responsibilities such as educating learners, conveying their knowledge, guiding students’ futures, and preparing learners for life. We have proven that the effective provision of this environment is related to teachers’ self-efficacy and mathematics teaching competencies. So, what can we do to create this environment?
We suggest that various activities and practices related to self-efficacy beliefs and teaching competency should be included in teacher training programs so that teacher candidates can use their teaching skills effectively in the classroom. So, what various activities can encourage the efficient use of our skills in the classroom? For example, it may be beneficial for pre-service teachers to create awareness by preparing a presentation on mathematics teaching competency, especially for the “Methods” course, which is one of the field courses, before starting their professional life.
In order to increase the awareness level of elementary school mathematics teacher candidates studying in education faculties, seminars can be organized about the perception of mathematics self-efficacy and mathematics teaching competency as an important factor in success.                   

Key Messages

  • Teachers’ self-confidence and self-efficacy skills are significant factors in providing more effective teaching to their students.
  • Pre-service mathematics teachers’ self-efficacy was higher than their mathematics teaching competencies.
  • Mathematics teachers’ self-efficacy seems to be the determining factor in their teaching styles and behaviour in the classroom and affects their teaching quality.
  • There was a significant and positive relationship between the pre-service mathematics teachers’ self-efficacy and their teaching competencies.
  • Teachers’ self-efficacy and teaching competencies should be sufficient for teaching in order for them to begin their professional careers properly.

Other blog posts on similar topics:

Büşra Uysal

Büşra Uysal

Büşra Uysal is a mathematics teacher. She graduated from MEF University, Istanbul. She gained teaching experience in both systems including face-to-face and online systems intensively. She received a Mentoring Certificate (2020-2021) and has been a supervisor for university students. In the scope of the “University within School” project, she did tutoring lessons with students. Her professional interests are to provide students with mathematical thinking skills and to create effective classroom environments where students can discover information and share their ideas freely.

She worked as a volunteer teacher at the Youth Education Center (Sarıyer Gençlik Eğitim Merkezi, Istanbul) within the “Social Responsibility Project” scope. In 2022, she conducted research on Pre-service Elementary Teachers’ Self-Efficacy for Teaching Mathematics & Teaching  Competency and presented at MEF University International Educational Sciences Student Conference (MEFEDUCON, 2022)

Dr Bengi Birgili

Dr Bengi Birgili

Research Assistant in the Mathematics Education Department at MEF University, Istanbul.

Dr Birgili is a research assistant in the Mathematics Education Department at MEF University, Istanbul. She experienced in research at the University of Vienna. Her research interests focus on curriculum development and evaluation, instructional design, in-class assessment. She received the Emerging Researchers Bursary Winners award at ECER 2017 for her paper titled “A Metacognitive Perspective to Open-Ended Questions vs. Multiple-Choice.”

In 2020, a co-authored research became one of the four accepted studies among Early-Career Scholars awarded by the International Testing Commission (ITC) Young Scholar Committee in the UK [Postponed to 2021 Colloquium due to COVID-19].

In Jan 2020, she completed the Elements of AI certification offered by the University of Helsinki.

Researchgate:https://www.researchgate.net/profile/Bengi-Birgili-2

Twitter: @bengibirgili

Linkedin: https://www.linkedin.com/in/bengibirgili/

ORCID:https://orcid.org/0000-0002-2990-6717

Medium: https://bengibirgili.medium.com

References and Further Reading

Bandura, A. (1995). Self-efficacy in changing societies. https://doi.org/10.1017/CBO9780511527692

Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman and Company Press.

Battista, M. T. (1986). The relationship of mathematics anxiety and mathematical knowledge to the learning of mathematical pedagogy by preservice elementary teachers. School Science and Mathematics, 86(1), 10–19. https://doi.org/10.1111/j.1949-8594.1986.tb11580.x 

Çakıroğlu, E., & Işıksal, M. (2009). Preservice elementary teachers’ attitudes and self-efficacy beliefs toward mathematics. Education and Science, 34, 151. https://hdl.handle.net/11511/52775

Esendemir, Ö., Çırak, S., & Samancıoglu, M. (2015). Pre-service elementary math teachers’ opinions about mathematics teaching competencies. Gaziantep University Journal of Social Sciences, 14(1), 217–239.https://doi.org/10.21547/jss.256787

Gavora, P. (2010). Slovak pre-service teacher self-efficacy: Theoretical and research considerations. The New Educational Review, 21(2), 17–30. https://www.researchgate.net/publication/287424468_Slovak_Pre-Service_Teacher_Self-Efficacy_Theoretical_and_Research_Considerations 

Gülten, D. Ç. (2013). An investigation of pre-service primary mathematics teachers’ math literacy self-efficacy beliefs in terms of certain variables. International Online Journal of Educational Sciences, 5(2), 393–408. https://iojes.net/?mod=makale_tr_ozet&makale_id=41128 

Küçükalioğlu, T., & Tuluk, G. (2021). The effect of mathematics teachers’ self-efficacy and leadership styles on students’ mathematical achievement and attitudes. Athens Journal of Education, 8(3), 221–238. https://doi.org/10.30958/aje.8-3-1 

Özgen, K., & Bindak, R. (2008). The development of a self-efficacy scale for mathematics literacy. Kastamonu Education Journal, 16(2), 517–528. https://doi.org/10.24106/kefdergi.413386

Stevens, C., & Wenner, G. (1996). Elementary preservice teachers’ knowledge and beliefs regarding science and mathematics. School Science and Mathematics, 96(1), 2–9. https://doi.org/10.1111/j.1949-8594.1996.tb10204.x 

Tosun, T. (2000). The beliefs of preservice elementary teachers toward science and science teaching. School Science and Mathematics, 100(7), 374–379. https://doi.org/10.1111/j.1949-8594.2000.tb18179.x

Zuya, H, E., Kwalat, S, K., & Attah, B, G. (2016). Pre-service teachers’ mathematics self-efficacy and mathematics teaching self-efficacy. Journal of Education and Practice, 7(14), 93–98. https://www.researchgate.net/publication/303723566_Pre-service_Teachers%27_Mathematics_Self-efficacy_and_Mathematics_Teaching_Self-efficacy 

Artificial Intelligence in Student Assessment: What is our Trajectory?

Artificial Intelligence in Student Assessment: What is our Trajectory?

Bengi Birgili is a Research Assistant in the Mathematics Education Department at MEF University in Istanbul. Here she shares her research and insights into the development of Artificial Intelligence applications in the field of education and explains the current trajectory of AI in the Turkish education system.

As a mathematics teacher and doctoral candidate in educational sciences, I closely follow the latest developments in Artificial Intelligence (AI) applications in the field of education. Innovations in AI become outdated within a few months because of the rapidly increasing studies on image processing, speech recognition, natural language processing, robotics, expert systems, machine learning, and reasoning. With Google, Facebook, and IBM AI studies being open source, these companies help speed up developments.

If we think of education as a chair, the legs are the four essential parts that keep it standing: that is, the student, the teacher, the teaching process, and measurement-evaluation – the four basic elements of education. Key areas of AI for education are determining the right strategies, making functional decisions, and coming up with the most appropriate designs for the education and training process. I believe there are many areas in which teachers can work in cooperation with Artificial Intelligence systems in the future.

Human behaviour modelling

The main focus of AI studies worldwide is human behavior modelling. The relationship between how humans model thinking and how we can, therefore, accurately measure and evaluate students is still a subject of exploration. Essentially, the question is: how do humans learn, and how can we teach this to AI expert systems?

Presently, AI expert systems learn in three ways:

  • supervised learning
  • unsupervised learning
  • reinforcement learning

As an educator, whenever I hear these categories, I think of the conditional learning and reward-punishment methods we learn about in educational sciences. These methods, which are prevalent at the most fundamental level in the individual teaching and learning process, are central to the design of AI systems being developed today, which are developed on the behavioristic approach in learning theories.

Just as in the classroom environment, where we can reinforce a students’ behavior by using a reward, praise, or acknowledgment in line with the behaviorist approach while teaching knowledge or skills so that we can strengthen the frequency of the behavior and increase the likelihood that how the response will occur. In a similar vein, an agent or a machine which is under development learns from the consequences of its actions.

AI in the Measurement-Evaluation Process

One area for the use of natural language processing in the measurement-evaluation process is the evaluation of open-ended examinations. In Turkey, large-scale assessment consists mostly of multiple-choice examinations, chosen for their broad scope, objective scoring, high reliability, and ease of evaluation. On the other hand, open-ended examinations are more challenging because they measure students’ higher-level thinking skills in much more detail than multiple-choice, fill-in-the-blanks, true-false, and short-answer questions.

Education systems in other countries make more use of open-ended items because they allow students to thoroughly use their reading comprehension skills. Also, students are able to demonstrate their knowledge in their own words and use multiple solution strategies, which is a better test of their content knowledge. But these open-ended items do not just measure students’ knowledge of a topic; at the same time, they mediate between higher-level thinking skills such as cognitive strategies and self-discipline. This is an area in which AI studies have begun to appear in the educational literature. 

Countries using open-ended items in new generation assessment systems are France, the Netherlands, Australia, and, in particular, the United States and the UK. These systems provide teachers, parents, and policymakers with the opportunity to monitor student progress based on student performance as well as student success. The development of Cognitive Diagnostic Models (CDM) and Computerized Adaptive Tests (CAT) changed testing paradigms. These models classify student response models in a test into a series of characteristics related to different hierarchically defined mastery levels. Another development is immersive virtual environments such as EcoMUVE, which can make stealth/invisible assessments, evaluating students’ written responses and automatically creating follow-up questions.

AI in Student Assessment in Turkey

It is a very broad concept that we call “artificial intelligence [AI] in education”. To simplify it, we can define it as a kind of expert system that sometimes takes the place of teachers (i.e., the intelligent tutors) by making pedagogical decisions about the student in the teaching or measurement-evaluation process. Sometimes the system assists by analyzing the student in-depth in the process, enabling them to interact with the system better. It aims to guide and support students. To make more computational, precise, and rigorous decisions in the education process, the field of AI and Learning Sciences collaborate and contribute to the development of adaptive learning environments and more customized, inclusive, flexible, effective tools by analyzing how learning occurs with its external variables.

Turkey is a country of tests and testing. Its education system relies on selection and placement examinations. However, developments in educational assessment worldwide include individual student follow-up, formative assessments, alternative assessments, stealth assessments, and learning analytics, and Turkey has yet to find its own trajectory for introducing AI in student assessment.

However, the particular structure of the Turkish language makes it more difficult than in other countries to design, model, develop, and test AI systems – which explains the limited number of studies being carried out. The development of such systems depends on big data, so it is necessary to collect a lot of qualified student data in order to pilot deep learning systems. Yet the Monitoring and Assessment of Academic Skills report of 2015-2018 noted that 66% of Turkish students do not understand cause and effect relationships in reading.

In AI testing, students are first expected to grasp what they read and then to express what they know in answering questions, to express themselves, to come up with solutions, and to be able to use metacognitive skills. The limited number of students who can clearly demonstrate these skills in Turkey limits the amount of qualified data to which studies have access. There is a long way to go in order to train AI systems with qualified data and to adapt to the complexities of the Turkish language. In short, Turkey is not yet on a trajectory for introducing AI for education measurement and evaluation – we are still working to get ourselves on an appropriate trajectory. We are still oscillating through the universe. However, there are signs that the future in this area will be designed faster, addressing the questions I have raised.

The Outlook for AI in Student Assessment

While designing and developing such systems, it should be remembered that students and teachers also need to adapt to the system. Their readiness to do so will help us measure the quality of education in general as well as the level of students’ knowledge and skills in particular. Authentic in-class examinations and national and international large-scale assessments should serve the same purpose. In the future, we will need AI systems to play a greater role in generating and categorizing questions and evaluating student responses. And they need to do this is a system whose main goal must be to provide a learning process that positively supports the curiosity and ability of all our students
Bengi Birgili

Bengi Birgili

Research Assistant in the Mathematics Education Department at MEF University, Istanbul.

Bengi Birgili is a research assistant in the Mathematics Education Department at MEF University, Istanbul. She experienced in research at the University of Vienna. She is currently a PhD candidate in the Department of Educational Sciences Curriculum and Instruction Program at Middle East Technical University (METU), Ankara. Her research interests focus on curriculum development and evaluation, instructional design, in-class assessment. She received the Emerging Researchers Bursary Winners award at ECER 2017 for her paper titled “A Metacognitive Perspective to Open-Ended Questions vs. Multiple-Choice.”

In 2020, a co-authored research became one of the 4 accepted studies among Early-Career Scholars awarded by the International Testing Commission (ITC) Young Scholar Committee in the UK [Postponed to 2021 Colloquium due to COVID-19].

In Jan 2020, she completed the Elements of AI certification offered by the University of Helsinki.

Researchgate:https://www.researchgate.net/profile/Bengi-Birgili-2

Twitter: @bengibirgili

Linkedin: https://www.linkedin.com/in/bengibirgili/

ORCID:https://orcid.org/0000-0002-2990-6717

Medium: https://bengibirgili.medium.com