The article delves into the transformative impact of mobile technology on the 5E learning model in physics laboratories, with a particular focus on enhancing graph drawing and interpretation skills. It begins by highlighting the rapid advancements in technology and their influence on educational practices, especially during the pandemic. The 5E learning model, which includes engagement, exploration, explanation, elaboration, and evaluation phases, is examined for its effectiveness in fostering meaningful learning experiences. The integration of mobile devices, such as tablets and smartphones, is explored for their potential to enrich the learning environment by providing real-time data visualization and interactive learning experiences. The article discusses the challenges and benefits of using mobile technology in educational settings, including issues related to connectivity, screen size, and battery life. It also provides a detailed analysis of the Graph Drawing and Interpretation Skills Test (GDIST), which was used to assess the effectiveness of the technology-enhanced learning model. The findings indicate that students who used mobile technology-integrated laboratory implementations showed significant improvements in their graph drawing and interpretation skills compared to those who used traditional laboratory tools. The article concludes by emphasizing the importance of incorporating graph literacy into educational curricula and providing recommendations for future research and practice. It offers a comprehensive overview of how mobile technology can be leveraged to enhance educational outcomes and prepare students for the demands of the 21st century.
AI Generated
This summary of the content was generated with the help of AI.
Abstract
This study, conducted within the scope of the General Physics Laboratory-II course, implemented instructional practices based on the 5E learning model for various physics topics (Ohm’s law, induction current, conductor resistance, magnetic fields, electrolysis, electric current, and transformers). The study followed a quasi-experimental design. Researchers examined the effects of two implementation processes carried out by 64 prospective science teachers (intervention group of 32 and a control group of 32) on graph drawing and interpretation skills. While the intervention group in this study was exposed to the 5E learning model supported by mobile technological devices, the control group received the 5E learning model supplemented with conventional laboratory equipment. To collect data during the implementations, the Graph Drawing and Interpretation Skills Test (GDIST), consisting of 21 open-ended questions requiring prospective teachers to draw and interpret graphs, was developed. The results indicated that enriched laboratory implementations with mobile technological devices were more effective in developing prospective teachers’ graph drawing and interpretation skills. Based on these results, several suggestions were made for researchers and science teachers who plan to use mobile technological devices in learning-teaching environments.
Notes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
As the authors of this study, we declare that it is an original work that has not been previously published and is not currently under consideration for publication elsewhere.
Introduction
Education is one of the areas affected by rapidly advancing technology (Balcı Çömez et al., 2021). Due to the pandemic declared in 2020, the teaching–learning process all over the world continued thanks to this ever-evolving technology (Coskun Karabulut et al., 2023). In this education-teaching process, devices such as computers, mobile phones, tablets, and internet hardware were the primary technological tools utilized. During this process, learning-teaching environments were conducted both synchronously and asynchronously. In asynchronous environments, students could follow and repeat lessons as often as they wanted, regardless of location, and at a time convenient for them (Stewart et al., 2013). By integrating such technologies into learning environments, students can achieve permanent and meaningful learning in accordance with their individual characteristics and learning speeds.
In today’s world, where science and technology are rapidly developing, societies attach great importance to science education to raise qualified individuals who research and question, know how to access information, have critical thinking skills, are sufficiently literate in technology, and possess positive attitudes and values (OECD, 2019a, 2019b). In the constructivist-oriented classroom, teachers value collaboration, student autonomy, productivity, reflection, and active participation (Becker & Riel, 1999; Hadley & Sheingold, 1993). Rapid advances in technology have enriched educational environments. This situation has led educators to focus on educational technology, curriculum, and teaching methods (Burušić et al., 2019). It is stated that the failure of innovations in curricula is generally due to the lack of understanding of teachers’ important roles in these reforms (Dori & Herscovitz, 2005) and the lack of knowledge of teachers on how to direct targeted activities (Sun et al., 2015). Teachers are effective in revealing students’ existing ideas and helping them form more accurate understandings (Holt-Reynolds, 2000). Teachers use technology as a cognitive tool to enrich student-centered curricula. One of the models based on the constructivist approach is the 5E model, developed by Roger Bybee (Ertmer et al., 2012). Over the past half-century, there has been a significant shift in the central role of information. The presentation of data in various forms and its use for informing or persuading has gained substantial importance (Ainley et al., 2000). Constructivist theory posits that students actively construct meaning by reconciling their existing experiences with prior knowledge (Bodner et al., 2001; NRC, 2000; Roth, 1993). Students are regarded as active learners who relate new ideas to their existing knowledge rather than being passive recipients of information. They build knowledge through interactions with the physical and social worlds before formal instruction (Glazer, 2011). The rapid increase in computing power and its accessibility have been pivotal factors in this transformation. Software tools such as databases and spreadsheets have emerged from the need to process raw data and represent it visually in diagrams, graphs, and charts (Ainley et al., 2000). According to the AAAS (1993), one criterion necessary for scientific literacy is that by the end of secondary education, students should be able to organize information in simple tables and graphs, identify relationships, interpret tables and graphs produced by others, and verbally explain their findings. This includes bar and line graphs, two-way data tables, diagrams, and symbols. As students advance in their education, they are expected to support their arguments and claims in oral and written presentations using tables, charts, and graphs. Additionally, it is emphasized that graphs should be carefully examined to ensure accurate representation of results, avoiding inappropriate scales or unclear axes (p.300). Visual representations of data often reveal elements not discernible in verbal explanations (Burke, 2007). The Criteria for Scientific Literacy (AAAS, 1993) state that the easiest way to understand quantitative descriptions of changes and rates of change is through graphs and tables. Graphs are widely used to display mathematical functions, visualize data from social and natural sciences, and express scientific theories in textbooks and other printed materials inside and outside the classroom (Kaput, 1987; Lewandowsky & Behrens, 1999). Furthermore, they facilitate the comprehension of quantitative information (MacDonald-Ross, 1977; Winn, 1987). Graphs play a significant role in intelligent tutoring systems and other educational software in teaching quantitative and scientific concepts (Nachmias & Linn, 1987; Quintana et al., 1999; Reiser et al., 2001; Shah & Hoeffner, 2002).
Advertisement
Theoretical Framework
The 5E Learning Model
The 5E learning model consists of five phases: the “engage” phase, where prior knowledge is revealed through curiosity; the “explore” phase, where existing misunderstandings are identified and resolved, and students take a more active role; the “explanation” phase, where the teacher explains concepts clearly and understandably; the “elaboration” phase, where students adapt these concepts to daily life; and finally, the “evaluation” phase, where students’ learning developments are assessed (Bybee, 2014). In the learning cycle approach, teachers are free to use various teaching methods and techniques at each stage of this model. Previous experiences can be easily integrated into the stages of the learning cycle (Scharmann, 1991). Campbell (1977) stated that learning cycle-based teaching, especially in laboratory courses, provides students with opportunities for concrete experiences, helps students develop their reasoning abilities, enhances social skills that help students master content, and supports their cognitive development (Liu et al., 2009; Martin, 2012). This model, in which argumentation, problem-solving, and other method techniques can be frequently used (Senan, 2013), can also be supported by various techniques such as computer animations, videos, and conceptual change texts (Şahin & Çepni, 2012) or computer-assisted instruction to provide richer learning-teaching environments (Demir & Emre, 2020). Research has shown that incorporating the computer-assisted 5E learning model into science education significantly enhances long-term retention compared to using the traditional 5E model alone for the concretization of abstract concepts (Akdeniz et al., 2017). In 2012, a significant educational project was initiated within the Turkish Education System to effectively integrate technology into classrooms, address the inequality of access to technological resources, and facilitate the seamless incorporation of technology into the educational environment (Güngör Seyhan, 2022). To achieve this goal, the FATIH (The Movement to Increase Opportunities and Improve Technology) Project, in collaboration with the Ministry of Transport, aimed to equip schools with technological resources and offer professional development programs for teachers on the effective use of this equipment (Aktas et al., 2014).
Mobile Technological Devices
Considered to enhance learning when combined with more student-centered teaching–learning environments (Falloon, 2013; Kucirkova et al., 2014), information and communication technology (ICT) includes laptops, wireless phones, and handheld devices. The rapid spread of these information processing devices has also changed the nature of higher education (Green, 2000). Studies on mobile teaching programs (Looi et al., 2014) indicate that research has predominantly focused on the design of mobile phone-supported teaching programs, the design of mobile learning environments, and the learning effectiveness of mobile teaching programs (Martin & Ertzberger, 2013). Active and interactive learning techniques such as M-learning (Kalinic et al., 2011) have been shown to help students acquire knowledge, develop critical thinking skills, solve problems in a variety of situations (Karatay et al., 2024), and think independently (Ratto et al., 2003). The use of mobile devices in teaching–learning environments has many benefits (Virvou & Alepis, 2005): spatial independence (Cmuk, 2007), teachers’ homework preparation behaviors and students’ homework completion behaviors in out-of-school settings with adequate equipment (Vavoula & Karagiannidis, 2005), providing students with “the ability to act independently in a free environment” (El-Hussein & Cronje, 2010) and motivating students to self-organize their learning (Sha et al., 2012). The portability and connectivity of mobile technologies allow students to carry handheld devices outside the classroom, collect data, and send records to databases for real-time analysis (Spain et al., 2001). Although the opportunities provided by m-learning are new, m-learning also faces several challenges such as connectivity issues, small screen sizes (El-Hussein & Cronje, 2010; Kalinic et al., 2011; Suki & Suki, 2011), limited processing and battery power (Riad & El-Ghareeb, 2008), and reduced input capabilities (Cheon et al., 2012), which can cause distractions and interruptions (Siau et al., 2001). It is also argued in the literature that learning with mobile devices cannot replace the classroom or other e-learning approaches (Motiwalla, 2007). It would be more accurate to say that m-learning serves as a complementary activity to both e-learning and traditional learning (Wang et al., 2009).
Mobile technologies are one of the fastest-growing areas of technology. They offer multiple opportunities for students to go beyond the information and/or approaches provided by the teacher by accessing many alternative sources of information. The development of such hardware, allowing pocket-sized mini-computers to be easily carried, and the integration of advanced wireless networks into teaching–learning environments have led to an increase in research on mobile devices (Ng & Nicholas, 2013).
Mobile Technology and Graphic Usage
In today’s world, where information is rapidly increasing with technology, those engaged in science use more visually appealing tools to present a large amount of data obtained within the scope of their studies in a shorter, simpler, more understandable, and impressive way (Aydın & Tarakçı, 2018). It is stated that one of the methods applied to make abstract concepts more understandable and concrete is the use of visuals (Kuvvetli Arpaguş et al., 2011). According to Aydın and Tarakçı (2018), graphics stand out among these visual materials (Gültekin & Nakiboğlu, 2015). Graphic organizers simply present data in a diagram (Farris, 2015, p.235) and structure knowledge by organizing the important elements of a concept and presenting them visually (Bromley et al., 1999). Guthrie et al. (1993) have defined graphics as “information conveyed by the position of points, lines, or areas on a two-dimensional surface.” This definition encompasses all spatial designs but excludes displays involving the use of symbols such as words and numbers (e.g., tables). In addition to this definition, graphics are invaluable tools in solving arithmetic and algebraic problems and representing relationships between variables (Ateş et al., 2003). Mathematical relationships not easily recognized numerically can be readily illustrated through graphics (Arkin & Colton, 1940). Graphics, an essential component in learning scientific content, are critical tools for data analysis and significant visual aids for understanding scientific and economic factors in daily life.
Advertisement
New interactive tools such as tablets and/or laptops, which fall under the scope of mobile devices, provide many advantages, such as allowing students to evaluate and/or simplify an expression with a single keystroke, solve an equation, or graph a function. They are particularly useful for comparing multiple graphs simultaneously and for showing many specific properties of the graphs involved (Coles, 1990). Graphing tools on mobile devices can replace these manual procedures with faster and more reliable ones by eliminating the sometimes lengthy process of calculating from available data and the effort of drawing correct/incorrect graphs manually (Güngör Seyhan, 2022; Güngör Seyhan & Okur, 2020, 2022; Okur, 2014). In this way, students are introduced to visual tools, namely graphs, allowing them to concretely observe variables that can change instantly and the relationships between these variables. Additionally, the literature indicates that graph technologies are particularly suitable for students who do not rely on symbolic processing skills (Hart, 1992). Graph technologies present a potential opportunity to enhance the role of graphing and visualization across the curriculum by addressing the critical need to combine visual and algebraic techniques (Demana & Waits, 1990; Dunham & Osborne, 1991). Graphic tools are very successful in eliminating the problems that can arise with multiple representations. With these tools, at least two related representations (graph + equation) can be worked on simultaneously, making the system more manageable (Hart, 1992; Scanlon et al., 2005). Some technologies, such as the tablets (NOVA5000) used in the applications with prospective science teachers in this study, offer the possibility of displaying the graph in such a way that the data table is built on top of it. In multi-representational software, as with these tablets, a change in one representation automatically triggers changes in another. Thus, a modification in a graph immediately results in a change in the values table and/or the algebraic definition of the function.
The graphs produced by the tablets, which are also included in the NOVA5000 experiment sets that provide the opportunity to compare two or more data sets, especially for the creation of a conceptual framework and summarization of the subject, provide many advantages in science teaching (Bowen & Roth, 2004; Güngör Seyhan & Okur, 2020, 2022; Taşdemir et al., 2005). Despite the fact that teachers use graphs as a scientific language in science lessons, research results show that this language is not used effectively by students (Beichner, 1994a, 1994b). Based on this basic information, this study examined the graph drawing and interpretation skills and science process skills of prospective science teachers in the physics laboratory environment realized with the help of mobile technological devices.
Graph Reading, Drawing, and Interpreting Skills
According to Dreyfus and Eisenberg (1990), reading a graph is a learned skill and cannot happen on its own. Bean et al. (1998) state that graphic literacy involves the ability to use graphical tools to interpret, read, present, and create visual presentations such as diagrams, maps, graphs, spreadsheets, timelines, cartoons, and photographs, which are used to enrich texts found in textbooks, non-theoretical commercial books, and newspapers. Wu (2004) asserts that existing studies in the literature on graphic literacy primarily examine individuals’ abilities in reading, drawing, and interpreting graphs. However, he emphasizes that graphic literacy should also encompass the skills of identifying errors within graphs and evaluating them (Yeniçeri & Bulut, 2023). Conceptual understanding of how to interpret and use graphics includes knowledge of graphical rules and techniques, such as the use of scale, as well as the practical skills necessary for manually producing graphics (Berg & Boote, 2017). Competence in graph creation and interpretation is considered crucial for developing scientific literacy (Roth et al., 1999) and understanding and communicating ideas in science (Barclay, 1985; Leinhardt et al., 1990; Linn et al., 1987). Constructing and interpreting data graphics are key components of the K- 12 statistics curriculum and are skills all high school students should possess (Glazer, 2011). In introductory college courses, students learn how to create data distributions using dot plots, histograms, and box plots. It is suggested that the understanding of graphics is influenced by their visual properties and various related abilities of the viewers, such as graph literacy, explanatory, and reasoning skills (Glazer, 2011; Shah & Hoeffner, 2002; Trafton & Trickett, 2001).
Students of all ages experience difficulty using graphs (Leinhardt et al., 1990). Despite being a vital component of literacy in the information age, graph reading and interpretation is a highly complex activity. One common cognitive error in the process of graph reading and interpretation is conflating data iconically, meaning perceiving the graph as a picture (Glazer, 2011; Leinhardt et al., 1990). Even university students struggle to interpret kinematic graphs (position, velocity, and acceleration vs. time) (Beichner, 1994a, 1994b). Recent research on statistics education documents the difficulties students face in learning to reason about distributions and their graphical representations (Bakker & Gravemeijer, 2004; Ben-Zvi, 2004; Hammerman & Rubin, 2004). Understanding graphs can require effort and be prone to error. Both school-aged children and adults frequently make systematic errors when interpreting graphs, especially when the graphs do not explicitly show quantitative information (Shah & Hoeffner, 2002). It has been reported that secondary school teacher candidates need more practice in developing their graph interpretation skills (Bowen & Roth, 2005). The ability to interpret and create graphs does not develop easily and should be nurtured from early elementary grades (Deniz & Dülger, 2012). Graphs are widely used in scientific applications and science classes. However, interpreting graphs is not always easy and these skills are not automatically acquired through exposure to graphs. Therefore, a clear instructional focus on graph interpretation is necessary (Glazer, 2011). To develop graph interpretation skills, science and mathematics educators should chart data analysis and related activities in the classroom. Educators should create practical programs that enhance graph creation and data analysis competence and offer opportunities to reflect on findings in the laboratory, helping to clarify understanding and misconceptions among peers (Roth & McGinn, 1997; Tobin, 1990). A laboratory education supported by mobile technology integrated with sensors is frequently used to overcome the challenges of graph interpretation. Studies show that students use this instruction to improve their ability to interpret line graphs representing physical phenomena such as motion, heat, and temperature (Barclay, 1985; Linn et al., 1987; Mokros & Tinker, 1987; Svec, 1995).
The ability to generate graphics with computers not only eliminates the need for traditional pen and paper skills but also allows for faster results (Shuard et al., 1991). This advancement paves the way for new subjects and learning methods. Organizing data in spreadsheets to be visualized as graphs emerges as a significant analytical skill (Ben-Zvi & Friedlander, 1997). Spreadsheet graphs can be created interactively and change dynamically when the underlying data is modified. The appearance of a spreadsheet graph, including features such as axis scales, direction, and the style of markers and labels, can be controlled through menus. While these features provide experienced users with a sense of control, they may cause novices to feel less confident (Ainley et al., 2000). In this study, graphs created through microcomputer-based laboratories (MBL) such as NOVA5000 experimental kits, which are one of the mobile technological devices integrated with sensors, are produced in real time during experiments by allowing students to directly participate in the experiments. Unlike static images, these graphs depict dynamic relationships and offer rapid feedback (Barclay, 1985; Linn et al., 1987; Mokros & Tinker, 1987; Thornton & Sokoloff, 1990). They serve as an effective tool for collecting, analyzing, and visualizing data obtained from scientific experiments. Data is collected directly using sensors or probes and is instantly displayed both numerically and graphically. By eliminating the drudgery of manual graph production, students are freed from time-consuming data collection activities and can easily examine the effects of changing variables due to the quick data collection and visualization. “Real-time” graphs allow students to ask and test their own “what if” questions rather than merely answering provided questions. This approach enables them to focus on graph interpretation, data evaluation, and scientific concepts (Krajcik & Layman, 1993). The effectiveness of this teaching method depends on the teacher’s knowledge of technology and relevant concepts, as well as their ability to relate students’ experiences to these concepts (Krajcik & Layman, 1993). In MBL-supported environments, students can monitor the graphs generated during the experimental process (Brasell, 1987; Mokros & Tinker, 1987), allowing them to establish a concrete connection between the physical phenomena and their graphical representation.
Graphical Literacy in Turkish Education System
In 2025, the Turkish education system, undergoing a major revision with The Century of Türkiye Education Model, emphasizes the importance of tables, graphs, figures, and diagrams in the preschool education curriculum. For a child to understand these materials correctly, make inferences, and share the data they collect by visualizing it with tables and graphs, it is essential to begin imparting skills in table, graph, figure, and diagram creation starting from the preschool period. The model stresses that introducing children to these tools early on can help them understand and interpret information effectively (MoNE, 2024a). In primary and secondary education, achievements related to graphs are also included in various subject contents. For instance, in the 3rd grade science course, one of the learning outcomes of the 6 th unit, “Electricity That Makes Our Lives Easier,” is “Predicting Efficient Use of Electricity Based on Scientific Data.” Students are asked to bring their electricity bills from previous months before the lesson. These bills are analyzed through group work, and students are expected to create a table or graph based on the electricity consumption data. Additionally, during the support phase of this learning outcome, data and/or graphs may be provided, or students may be asked to record the data through group work. In the 8 th grade science course, one of the learning outcomes of the 3rd unit, “The Mystery of Life,” is “Discussing the Genetic Consequences of Consanguineous Marriages.” Students brainstorm and discuss their thoughts on the genetic consequences of consanguineous marriages using various techniques. They are then divided into groups to discuss the data through visual aids such as graphs and tables. The 6 th unit at the same grade level is “The Journey of Electricity.” One of the learning outcomes of this unit is “Inductive Reasoning the Relationship Between Voltage Across a Circuit Element and the Current Passing Through It.” Students are asked to determine the number of bulbs as the control variable and the number of batteries as the independent variable in their experimental setups. Using a scientific approach, students measure the potential difference and current magnitude in the electrical circuit they set up with a voltmeter and ammeter, and they record the current–voltage relationship in their experiment reports through graphs.
Additionally, students are expected to reach Ohm’s law by analyzing the graph data they have created and performing mathematical modeling (MoNE, 2024b). In the Turkish education system, students in the 1 st grade mathematics course learn the steps of the statistical research process within the 7 th theme, “Data-Based Research.” This theme aims for students to work with a basic data set based on categorical data and make data-driven decisions. One of the social-emotional learning skills related to this learning outcome is emphasized as follows: After completing the data visualization step, students are asked to interpret the graph by matching objects and data without performing numerical operations, by asking questions such as “What can you say about the graph? Which is more/less?” Students at this grade level are asked to evaluate the extent to which the research results answer the research questions by being asked questions about what they understand from the graph. During this process, students’ object-data matching can be evaluated with fill-in-the-blank questions. Various examples are given to students in the classroom, showing that the interpretation of graphs prepared with different data is done according to the existing data and that the interpretations are presented only within the context of that graph. The primary school mathematics curriculum, from 1 st grade to the end of 4 th grade, includes themes that require similar skills from students within the framework of “Data-Based Research.” In the middle school mathematics curriculum, from the 5 th grade level to the end of the 8 th grade, students are expected to “create graphs based on data obtained in mathematical field skills, read and interpret existing graphs” (MoNE, 2024c). The social studies course, within the 5 th learning area of the 4 th grade, titled “Economy in Our Life,” aims to equip students with various skills. One of the fundamental skills in this learning area is “Reading and Interpreting Tables, Graphs, Figures, and Diagrams.” Within this framework, students are expected to achieve the learning outcome of “being able to interpret graphs related to the consumption of natural resources.” The social studies course from the 4 th grade to the end of the 8 th grade contains similar skill-based learning outcomes (MoNE, 2024d). Moreover, under the Turkish education system, the Turkish language curriculum at each grade level requires students to present objects, concepts, or events in appropriate formats (lists, tables, graphs, figures, etc.) (MoNE, 2024e). In summary, graphs will be frequent elements throughout our educational journey. While understanding and interpreting these elements, the initial “reading” skill holds significance. However, in the current era, possessing just this skill is insufficient. When examining the achievements of different courses, it is evident that higher-level skills, such as interpreting graphs, beyond just reading them, are required as grade levels progress (Akyol, 2012; PISA, 2012, 2018).
In the twenty-first century, within the production-based model referred to as Industry 4.0, it is a fundamental goal of the education policies of many nations to cultivate innovative, adaptable, and competitive individuals. These individuals not only read and comprehend information but also transfer, transform, and model this information in an interdisciplinary manner in daily life (MOE, 2021; Ontario, 2021). Therefore, graphic literacy skills should be based on contemporary and functional reasons that promote personal development within educational curricula. Achieving this causality accurately and integrating it into quality teaching will yield the desired outcomes. Hence, preparing students at an early age to proficiently use graphs at all educational levels will add multidimensionality to educational policies and contribute to the goal of nurturing functional individuals needed both nationally and globally. An individual proficient in graph literacy can translate the same information from science to mathematics, from mathematics to social studies, and from social studies to language arts. They can also comprehend how data in any field is generated, understand the distinction between reality and perception (OECD, 2019a, 2019b), and assess its consistency. Based on this understanding of reality and consistency, such individuals can make informed shopping decisions, form opinions on various aspects of their daily lives, detect faulty or misleading graphs, and are less likely to be easily deceived. Therefore, fostering individuals with graph literacy skills is of paramount importance. When examining studies comparing closed-ended experiments using a deductive approach and open-ended experiments using inductive approaches, it has been concluded that approaches in which students take on investigative, questioning, and exploratory roles are more effective in increasing academic achievement (Gültepe, 2016; Güngör Seyhan, 2022; Güngör Seyhan & Okur, 2022; Karatay et al., 2024). For this reason, the activities designed for both groups were conducted within the same learning-teaching method but with different content in this study. The researchers aimed to examine the impact of integrated sensors with mobile technology on the targeted improvements in prospective teachers’ graph reading and interpretation skills by the end of the study. If the conventional laboratory equipment used with the control group had been conducted using a more closed-ended experimental model different from the 5E learning model to the intervention group, there would have been doubt as to whether the observed changes in graph reading and interpretation skills at the end of the study were due to the learning method or the differences in the tools used in the content. Therefore, all variables that could affect the potential development in these skills were controlled: Similar experimental implementations on the same physics topics were conducted for both groups using the same learning method. In line with this purpose, this study sought to answer the basic problem statement: “What is the effect of two 5E learning models supported by different contents on prospective teachers’ graph drawing and interpretation skills?”.
Method
Research Design
In the present study, a quasi-experimental design was used to examine the effectiveness of two 5E learning model supported by different contents on the graph drawing and interpretation skills of prospective science teachers. Quasi-experimental research designs are a type of research methodology that resembles true experimental designs but do not involve the random assignment of participants to groups (Creswell, 2012). In the study, activities designed according to both 5E learning models enriched with mobile technological and conventional laboratory equipment were prepared with content suitable for the General Physics Laboratory-II course curriculum. The participants to be included in the study are second-year students of Science Education who are taking this relevant course. There are two sections at the second-year level in this curriculum. Therefore, the prospective teachers studying in both sections are essentially in pre-formed group statuses. In educational research, it is often not possible for researchers to conduct real experimental studies. The most important reason for this is the impossibility of random assignment of individuals to groups in school and classroom settings (Büyüköztürk, 2007). In this case, the only thing to do is to randomly decide that one of the pre-formed groups will be the intervention group and the other will be the control group.
Study Group
The study group of the research consists of prospective science teachers taking the General Physics Laboratory-II course. The validity and reliability study of the Graph Drawing and Interpretation Skills Test (GDIST) used within the scope of the study was carried out with 49 s-year prospective science teachers studying in the academic year three semesters before the actual implementations. Preliminary studies of the guide materials designed to be applied to the intervention group were carried out with 32 first-year prospective science teachers who studied in the previous academic year. The actual implementations of the study were carried out with 64 (32 + 32) prospective teachers taking the General Physics Laboratory-II course. The activities designed within the scope of the study are aimed at teaching the concepts, phenomena, or events that should be covered in the General Physics Laboratory-II course, which second-year prospective teachers are required to take. Therefore, since the study will be applied to second-year prospective teachers, purposive sampling, one of the non-probability sampling methods, was used in the selection of the sample. However, the selection of the sample regarding which section would be the intervention group and which would be the control group was determined by random sampling. Both the intervention group and the control group prospective teachers carried out the activities in groups of 3–4 people.
Data Collection Tools
Graph Drawing and Interpretation Skills Test
In this study, an assessment tool consisting of 21 open-ended questions that require prospective teachers to draw and interpret graphs was developed by the researchers within the scope of the General Physics Laboratory-II course. Recording and categorizing data is necessary to make sense of the experimental data in physics laboratory courses. Developing graph drawing and interpretation skills is crucial for associating and presenting the obtained data (Taşar et al., 2002; Temiz & Tan, 2009). When the literature is reviewed, it is seen that most studies on graphs use multiple-choice tests (Gabel, 1993; Taşar et al., 2002). Taşar et al. (2002) and Coward (1981) stated that there are difficulties in measuring and evaluating the skills of drawing and understanding graphs with multiple-choice tests. They reported that the reliability and validity of a measurement tool composed of open-ended questions would be higher. The validity of this assessment tool, which consists of open-ended questions, was enhanced by obtaining expert opinions. Based on expert opinions, rubrics were developed for evaluating the test. Since the assessment of the tool was conducted using rubrics, the reliability was determined by calculating the Cronbach Alpha value. As a result of the evaluation conducted with the SPSS 21 software package, the Cronbach Alpha value was found to be 0.77. The preliminary studies of the assessment tool were conducted with second-year teacher candidates in the previous academic year. Following the preliminary studies, some items that prospective teachers found difficult to understand were revised. The distribution of the graph drawing and interpretation questions in the test according to the activities is presented in Table 1.
Table 1
Question distributions of graph drawing and interpretation skills test
Implementation Process of the Intervention Group
Initially, a literature review was conducted on the use of the 5E learning model in physics laboratory implementations and the use of sensors integrated with mobile technology in laboratory environments. Based on the review, it was determined that activities developed using the 5E learning model for the physics laboratory are available in the literature, but activities developed using mobile technological devices are not widely used in physics laboratory environments. In the research, Windows CE-based NOVA 5000 experiment systems distributed to schools by the Turkish Education System for use in primary and secondary education levels were used as mobile technological devices. Within this context, it was decided to prepare guidance materials, including mobile technological applications based on the 5E learning model for the General Physics Laboratory-II course. As a result of the preliminary studies of the developed guidance materials and data collection tool, necessary arrangements were made and applied for the actual study.
In the development of the activities in the guidance materials for the intervention group, worksheets were utilized. The introduction, exploration, and evaluation stages of the 5E learning model were integrated into the worksheets, and worksheets for six activities (Ohm’s law, magnetism, induction current, resistance of a conductor, electrolysis, and transformer) were developed. In the guidance materials for prospective teachers, the experiments to be carried out during the teaching process, the rules to be followed in the laboratory environment, the introduction and use of mobile technology devices and sensors used in the experiments, data collection, recording, and the use of computer software for drawing graphs were introduced. The worksheet prepared for the subject of “Conductor resistance” and having eight-step instructions is given in Fig. 1.
Fig. 1
Some stages of the guidance material prepared for prospective teachers on the subject of “Resistance of a Conductor”
The guidance materials for instructors of the intervention group contain suggestions about the procedures to be followed by the prospective teachers in the introduction, exploration, and evaluation stages, as well as information about the activities to be carried out in the explanation and elaboration stages of the 5E learning model. The “Engage, Explore, and Explain” stages of the guidance materials prepared for instructor on the topic of Ohm’s law are presented in Fig. 2.
Fig. 2
Some stages of the guidance material prepared for instructor on the subject of “Ohm’s law”
During the preliminary studies of the developed guidance materials, observations and student opinions were taken into consideration, and the missing parts were identified and addressed.
Implementation Process of the Control Group
The laboratory handbook containing the activities (Ohm’s law, magnetic field, induction current, resistance of a conductor, electrolysis, and transformer) prescribed by the Council of Higher Education (in Türkiye) for the General Physics Laboratory-II course was taken as a basis and applied to the control group students. These activities aim at teaching the concepts, facts, and events within the scope of Physics subjects intended for the intervention group. The prospective teachers in the control group carried out experiments in accordance with the 5E model, which included manual measuring instruments. The laboratory handbook contains activities designed to motivate students to observe, compare, build models, make predictions, and improve their skills to test those predictions. Some stages of guidance materials prepared for the control group (prospective teacher) on the topic of the “Magnetic Field” are given in Fig. 3.
Fig. 3
Some stages of guidance materials prepared for the control group (prospective teacher) on the topic of the “Magnetic Field”
Analysis of the Findings Obtained from the Graph Drawing and Interpretation Skills Test
For the analysis of the data obtained from the Graph Drawing and Interpretation Skills Test, which consists of 21 open-ended questions, categories were created for the graphic drawings and interpretations of the prospective teachers. These categories were then scored. For the graph drawing part of this scoring, the criteria in Table 2, which were created by referring to the studies by Taşar et al. (2002) and Temiz and Tan (2009), were taken into consideration. Upon reviewing the literature, it was found that there are studies that employ similar scoring methods in the assessment of students’ graph drawing and reading skills (Aydın & Tarakçı, 2018; Erbilgin et al., 2015; Yurtseven Yılmaz et al., 2023).
Table 2
Assessment criteria for graph drawing category skills
Operation
Score
Selection and partitioning of coordinate axes
1
Estimating the graph from the data
1
Coordinate detection
1
Curve fitting to data
1
The relationship between the axes of a graph drawn
1
Unitization of axes
1
Table 2 presents the evaluation criteria used for creating graph drawing categories. The graph drawings made by the prospective teachers were scored according to these criteria, and the categories in Table 3 were formed based on the total scores obtained.
Table 3
Graph drawing skills categories
Category
Explanation
Score
Correct Graph
Scoring 5–6 points according to the evaluation criteria
3
Partially Correct Graph
Scoring 3–4 points according to the evaluation criteria
2
Incorrect Graph
Scoring 1–2 points according to the evaluation criteria
1
Empty
Scoring 0 points according to the evaluation criteria
0
According to Table 3, the category of drawing graphs is divided into four categories: “Correct Graph (CG),” “Partially Correct Graph (PCG),” “Incorrect Graph (IG),” and “Empty.” These categories were scored based on the scores obtained from the evaluation criteria. For the graph drawing section of the GDIST, a prospective teacher can achieve a maximum score of 63 points. The evaluation categories for the graph interpretation section of the GDIST are listed in Table 4.
Table 4
Graph interpretation skills categories
Category
Explanation
Score
Correct Interpretation (CI)
The category with scientifically correct interpretations
3
Partially Correct Interpretation (PCI)
The category that contains scientifically correct interpretations but is not considered to be completely correct
2
Incorrect Interpretation (II)
The category of scientifically incorrect interpretations
1
Empty
Category left empty with no interpretation
0
In Table 4, the interpretations of the graphs drawn by the prospective teachers are categorized as “Correct Interpretation (CI),” “Partially Correct Interpretation (PCI),” “Incorrect Interpretation (II),” and “Empty.” Four categories were formed and scored based on the scientific accuracy of the statements. The maximum score a prospective teacher could achieve for this part of the test was 63.
The data obtained from scoring the criteria and categories, as determined by the researchers for prospective teachers’ graph drawing and interpretation skills, were analyzed using SPSS 21 software. First, the Kolmogorov–Smirnov test was conducted to determine whether the data set followed a normal distribution. It was concluded that parametric tests would be used, as the data set showed a normal distribution and the sample size was over 30. Comparisons within the intervention and control groups were made using a dependent t-test, while comparisons between groups were made using an independent t-test.
Findings
Findings for Graph Drawing Skills
The basic problem of the study is as follows: “What is the effect of two different 5E learning models supported by different content on prospective teachers’ graph drawing and interpretation skills?” The Graph Drawing and Interpretation Skills Test (GDIST) was administered as both a pre-test and post-test to gather data for this sub-problem. Table 5 presents the frequency and percentage distributions of the scores obtained by the prospective teachers in the drawing section of the GDIST.
Table 5
Frequency and percentages related to graph drawing skills
Groups
CG
PCG
IG
Empty
Pre-
Post-
Pre-
Post-
Pre-
Post-
Pre-
Post-
f
%
f
%
f
%
f
%
f
%
f
%
f
%
f
%
Q1
Control
3
9.4
10
31.3
11
34.4
14
43.8
13
40.6
8
25.0
5
15.6
0
0
Intervention
0
0
32
100
10
31.3
0
0
15
46.9
0
0
7
21.9
0
0
Q2
Control
1
3.1
9
28.1
11
34.4
11
34.4
15
46.9
10
31.3
5
15.6
2
6.3
Intervention
0
0
28
87.5
5
15.6
1
3.1
21
65.6
3
9.3
6
18.8
0
0
Q3
Control
0
0
0
0
0
0
0
0
10
31.3
22
68.8
22
68.8
10
31.3
Intervention
0
0
29
90.6
0
0
3
9.4
1
3.1
0
0
31
96.9
0
0
Q4
Control
0
0
0
0
0
0
0
0
6
18.8
20
62.5
26
81.3
12
37.5
Intervention
0
0
29
90.6
0
0
3
9.4
1
3.1
0
0
31
96.9
0
0
Q5
Control
0
0
0
0
0
0
0
0
3
9.4
16
50.0
29
90.6
16
50.0
Intervention
0
0
27
84.3
0
0
5
15.6
1
3.1
0
0
31
96.9
0
0
Q6
Control
0
0
0
0
0
0
0
0
2
6.3
15
46.9
30
93.8
17
53.1
Intervention
0
0
27
84.3
0
0
5
15.6
2
6.3
0
0
30
93.8
0
0
Q7
Control
0
0
0
0
0
0
0
0
1
3.1
7
21.8
31
96.9
25
78.1
Intervention
0
0
24
75.0
0
0
4
12.5
1
3.1
4
12.5
31
96.9
0
0
Q8
Control
0
0
2
6.3
0
0
4
12.5
1
3.1
2
6.3
31
96.9
24
75.0
Intervention
0
0
27
84.3
0
0
5
15.6
1
3.1
0
0
31
96.9
0
0
Q9
Control
0
0
2
6.3
0
0
3
9.4
1
3.1
6
18.8
31
96.9
21
65.6
Intervention
0
0
22
68.7
0
0
2
6.3
1
3.1
2
6.3
31
96.9
6
18.8
Q10
Control
0
0
2
6.3
0
0
10
31.3
2
6.3
8
25.0
30
93.8
12
37.5
Intervention
0
0
23
71.8
0
0
1
3.1
3
9.4
3
9.4
29
90.6
5
15.6
Q11
Control
0
0
2
6.3
0
0
6
18.8
1
3.1
9
28.1
31
96.9
15
46.9
Intervention
0
0
26
81.2
0
0
3
9.4
2
6.3
3
9.4
30
93.8
0
0
Q12
Control
0
0
0
0
0
0
8
25.0
1
3.1
7
21.9
31
96.9
17
53.1
Intervention
0
0
20
62.5
0
0
4
12.5
1
3.1
0
0
31
96.9
8
25.0
Q13
Control
0
0
0
0
0
0
6
18.8
0
0
8
25.0
32
100
18
56.3
Intervention
0
0
25
78.1
0
0
7
21.8
0
0
0
0
32
100
0
0
Q14
Control
0
0
0
0
0
0
3
9.4
0
0
8
25.0
32
100
21
65.6
Intervention
0
0
19
59.3
0
0
3
9.4
0
0
2
6.3
32
100
8
25
Q15
Control
0
0
0
0
0
0
2
6.3
0
0
8
25.0
32
100
22
68.8
Intervention
0
0
24
75
0
0
4
12.5
0
0
3
9.4
32
100
1
3.1
Q16
Control
0
0
0
0
0
0
2
6.3
0
0
7
21.9
32
100
23
71.9
Intervention
0
0
27
84.3
0
0
4
12.5
2
6.3
0
0
30
93.8
1
3.1
Q17
Control
0
0
0
0
0
0
5
15.6
1
3.1
7
21.9
31
96.9
20
62.5
Intervention
0
0
26
81.2
0
0
6
18.8
2
6.3
0
0
30
93.8
0
0
Q18
Control
0
0
0
0
0
0
2
6.3
1
3.1
8
25.0
31
96.9
22
68.8
Intervention
0
0
26
81.2
0
0
6
18.8
1
3.1
0
0
31
96.9
0
0
Q19
Control
0
0
0
0
0
0
3
9.4
1
3.1
6
18.8
31
96.9
23
71.9
Intervention
0
0
27
84.3
0
0
5
15.6
2
6.3
0
0
30
93.8
0
0
Q20
Control
0
0
0
0
0
0
2
6.3
1
3.1
9
28.1
31
96.9
21
65.6
Intervention
0
0
29
90.6
0
0
3
9.4
1
3.1
0
0
31
96.8
0
0
Q21
Control
0
0
0
0
0
0
4
12.5
2
6.3
6
18.8
30
93.8
22
68.8
Intervention
0
0
29
90.6
0
0
3
9.4
2
6.3
0
0
30
93.8
0
0
According to Table 5, while it was observed that students from both groups did not provide answers in the CG category in the pre-test of the GDIST, the answers given by the intervention group in this category after the implementation were particularly notable. It was also determined that the frequency of answers given by the intervention group increased in the post-test. Another noteworthy finding from the GDIST data was observed in the PCG category. The percentage distribution of answers given by the intervention group decreased in the IG category. In the graph drawing part of the GDIST, it was observed that both groups of students provided a high number of answers in the Empty category in the pre-test. This ratio decreased in favor of the intervention group in the post-test. Within the scope of the study, pre-test and post-test scores were directly used to compare the data obtained from the control and intervention groups for the graph drawing skills of the GDIST applied to prospective teachers. In the evaluation of the groups within themselves, the gain scores, which express the difference between the pre-test and post-test scores, were used.
The pre- and post-test graph drawing skill scores of both groups were compared, and the results are presented in Table 6.
Table 6
Independent t-test results for the graph drawing skills of the pre and post-tests of the GDIST
GDIST
Group
N
\(\overline{X }\)
ss
sd
t
p*
Pre-test
Control
32
3.68
1.99
62
1.960
0.055
Intervention
32
2.81
1.55
Post-test
Control
32
14.00
9.34
62
− 20.765
0.000*
Intervention
32
56.81
6.97
*There is a significant difference at p < 0.05 level
As seen in Table 6, the results for the pre-test scores of both groups showed no statistically significant difference in graph drawing skills before the implementations (t = 1.960, p > 0.05I. This finding indicates that the graph drawing skill levels of the control and intervention groups were similar before the implementation. However, when the post-test scores were examined, a significant difference was found in favor of the intervention group (t = − 20.765, p < 0.05).
The pre- and post-test graph drawing skills scores of both groups were also compared, and the results are presented in Table 7.
Table 7
Dependent t-test results between the pre-test and post-test of the graph drawing skills part of the GDIST
Group
GDIST
N
\(\overline{X }\)
ss
sd
t
p
Control
Pre-test
32
3.68
1.99
31
− 6.696
0.000*
Post-test
14.00
9.34
Intervention
Pre-test
32
2.81
1.55
31
− 43.739
0.000*
Post-test
56.81
6.97
*There is a significant difference at p < 0.05 level
As seen in Table 7, the results for the pre- and post-test scores of both groups showed a statistically significant difference in favor of the post-test scores in terms of graph drawing skills (t = − 6.696; t = − 43.739; p < 0.05). This finding indicates that the implementation in both groups improved the graph drawing skills of the prospective teachers.
When the pre- and post-test graph drawing skills scores were analyzed, it was evident that the implementation in both groups was effective in improving the graph drawing skills of the prospective teachers. To reveal the difference between the two groups, the gain scores (differences between the pre-test and post-test scores) were analyzed. The results are presented in Table 8.
Table 8
Independent t-test results between the graph drawing skills partial gain scores in the GDIST
Group
N
\(\overline{X }\)
ss
sd
t
p
GAIN
Control
32
10.31
8.71
62
− 22.133
0.000*
Intervention
32
54.00
6.98
*There is a significant difference at p < 0.05 level
As seen in Table 8, the results for the gain scores of both groups showed a statistically significant difference in favor of the intervention group in terms of graph drawing skills (t = − 21.327, p < 0.05). Although this finding indicates that the implementation in the control group improved the graph drawing skills of the prospective teachers, it is concluded that the materials developed with the help of sensors integrated with mobile technology, applied in the intervention group, were much more effective in enhancing the graph drawing skills of the prospective teachers. When evaluating the effect of laboratory implementations integrated with mobile technology on prospective teachers’ graph drawing skills in terms of pre- and post-test scores of the both groups, the situation is illustrated in Graph 1.
Graph 1
Comparison of pre- and p[RV1] ost-test mean scores of graphs drawing skills
The frequencies and percentages of prospective teachers’ answers for each item in the graph interpretation part of the GDIST are shown in Table 9.
Table 9
Frequencies and percentages related to graph interpretation skills
Groups
CI
PCI
II
Empty
Pre-
Post-
Pre-
Post-
Pre-
Post-
Pre-
Post-
f
%
f
%
f
%
f
%
f
%
f
%
f
%
f
%
Q1
Control
1
3.1
3
9.4
5
15.6
11
34.4
16
50
8
25.0
10
31.3
10
31.3
Intervention
0
0
32
100.0
5
15.6
0
0
11
34.4
0
0
16
50
0
0
Q2
Control
0
0
5
15.6
10
31.3
11
34.4
10
31.3
7
21.9
12
37.5
9
28.1
Intervention
0
0
30
93.8
3
9.4
1
3.1
14
43.8
1
3.1
15
46.9
0
0
Q3
Control
0
0
0
0
0
0
0
0
6
18.8
18
56.3
26
81.3
14
43.8
Intervention
0
0
27
84.4
0
0
5
15.6
1
3.1
0
0
31
96.9
0
0
Q4
Control
0
0
0
0
0
0
0
0
4
12.5
17
53.1
28
87.5
15
46.9
Intervention
0
0
27
84.4
0
0
5
15.6
1
3.1
0
0
31
96.9
0
0
Q5
Control
0
0
0
0
0
0
0
0
3
9.4
14
43.8
29
90.6
18
56.3
Intervention
0
0
26
81.3
0
0
5
15.6
1
3.1
1
3.1
31
96.9
0
0
Q6
Control
0
0
0
0
0
0
0
0
2
6.3
13
40.6
30
93.8
19
59.4
Intervention
0
0
26
81.3
0
0
5
15.6
2
6.3
1
3.1
30
93.8
0
0
Q7
Control
0
0
1
3.1
0
0
1
3.1
0
0
5
15.6
32
100
25
78.1
Intervention
0
0
23
71.9
0
0
1
3.1
1
3.1
2
6.3
31
96.9
6
18.8
Q8
Control
0
0
1
3.1
0
0
4
12.5
1
3.1
6
18.8
31
96.9
21
65.6
Intervention
0
0
26
81.3
1
3.1
3
9.4
0
0
2
6.3
31
96.9
1
3.1
Q9
Control
0
0
0
0
0
0
9
28.1
1
3.1
3
9.4
31
96.9
20
62.5
Intervention
0
0
24
75.0
0
0
1
3.1
1
3.1
1
3.1
31
96.9
6
18.8
Q10
Control
0
0
1
3.1
0
0
14
43.8
4
12.5
6
18.8
28
87.5
11
34.4
Intervention
0
0
25
78.1
0
0
2
6.3
3
9.4
0
0
29
90.6
5
15.6
Q11
Control
0
0
1
3.1
0
0
12
37.5
1
3.1
7
21.9
31
96.9
12
37.5
Intervention
0
0
27
84.4
0
0
3
9.4
2
6.3
1
3.1
30
93.8
1
3.1
Q12
Control
0
0
1
3.1
0
0
11
34.4
1
3.1
3
9.4
31
96.9
17
53.1
Intervention
0
0
20
62.5
0
0
5
15.6
1
3.1
0
0
31
96.9
7
21.9
Q13
Control
0
0
0
0
0
0
7
21.9
0
0
6
18.8
32
100
19
59.4
Intervention
0
0
26
81.3
0
0
4
12.5
0
0
1
3.1
32
100
1
3.1
Q14
Control
0
0
1
3.1
0
0
5
15.6
1
3.1
5
15.6
31
96.9
21
65.6
Intervention
0
0
23
71.9
0
0
3
9.4
0
0
0
0
32
100
6
18.8
Q15
Control
0
0
1
3.1
0
0
4
12.5
0
0
8
25.0
32
100
19
59.4
Intervention
0
0
22
68.8
0
0
5
15.6
0
0
4
12.5
32
100
1
3.1
Q16
Control
0
0
0
0
0
0
5
15.6
0
0
3
9.4
32
100
24
75.0
Intervention
0
0
28
87.5
0
0
3
9.4
2
6.3
0
0
30
93.8
1
3.1
Q17
Control
0
0
0
0
0
0
11
34.4
2
6.3
2
6.3
30
93.8
19
59.4
Intervention
0
0
30
93.8
0
0
2
6.3
3
9.4
0
0
29
90.6
0
0
Q18
Control
0
0
0
0
1
3.1
3
9.4
1
3.1
6
18.8
30
93.8
23
71.9
Intervention
0
0
29
90.6
0
0
1
3.1
1
3.1
2
6.3
31
96.9
0
0
Q19
Control
0
0
0
0
0
0
0
0
0
0
10
31.3
32
100
22
68.8
Intervention
0
0
24
75.0
0
0
6
18.8
1
3.1
1
3.1
31
96.9
1
3,1
Q20
Control
0
0
0
0
0
0
0
0
0
0
8
25.0
32
100
24
75.0
Intervention
0
0
27
84.4
0
0
3
9.4
0
0
2
6.3
32
100
0
0
Q21
Control
0
0
0
0
0
0
1
3.1
2
6.3
6
18.8
30
93.8
25
78.1
Intervention
0
0
26
81.3
0
0
3
9.4
1
3.1
2
6.3
31
96.9
1
3.1
According to Table 9, no responses were observed from the students of either group in the CI category in the pre-test of the GDIST. However, an increase in the number of responses was noted in the post-test, favoring the intervention group. A similar trend was observed in the PCI category. In the II category, a decrease in responses was observed for both groups in the post-test, with the decrease being more pronounced in the intervention group. In the Empty category, it was found that students from both groups provided a high number of responses in the pre-test, but this rate decreased in the post-test, again favoring the intervention group.
While the pre-test and post-test scores were directly used to compare the control and intervention groups, the gain scores, representing the difference between the pre- and post-test scores, were used for within-group evaluations. The pre- and post-test graph interpretation skills scores of both groups were compared, and the results are presented in Table 10.
Table 10
Independent t-test results for the pre-test and post-test for graph interpretation skills in the GDIST
GDIST
Group
N
\(\overline{X }\)
ss
sd
t
p
Pre-test
Control
32
2.81
2.16
62
1.622
0.110
Intervention
32
2.00
1.83
Post-test
Control
32
13.25
9.88
62
− 18.580
0.000*
Intervention
32
56.15
8.53
As a result of the independent t-test conducted for the pre-test scores of both groups, no statistically significant difference was found between the groups in terms of graph interpretation skills before the implementations (t = 1.622, p > 0.05). This finding indicates that the control and intervention groups were at a similar level in graph interpretation skills before the implementation. However, when the post-test scores of both groups were examined, a significant difference was found in favor of the intervention group (t = − 18.580, p < 0.05).
The pre- and post-test graph interpretation skills scores of both groups were also compared, and the results are presented in Table 11.
Table 11
Dependent t-test results between the pre-test and post-test for the graph interpretation skills part of the GDIST
Group
GDIST
N
\(\overline{X }\)
ss
sd
t
p
Control
Pre-test
32
2.81
2.16
31
− 6.375
0.000*
Post-test
13.25
9.88
Intervention
Pre-test
32
2.00
1.83
31
− 36.216
0.000*
Post-test
56.15
8.53
*There is a significant difference at p < 0.05 level
As seen in Table 11, the results for the pre- and post-test scores of both groups showed a statistically significant difference in favor of the post-test scores in terms of graph interpretation skills (t = − 6.375; t = − 36.216; p < 0.05). This finding indicates that the implementation in both groups improved the graph interpretation skills of the prospective teachers.
When the pre- and post-test graph interpretation skills scores of both groups were examined, it was evident that the implementation in both groups was effective in improving the graph interpretation skills of the prospective teachers. To reveal the difference between the two groups, the gain scores were analyzed. The results are presented in Table 12.
Table 12
Independent t-test results between the gain scores for the graph interpretation skills part of the GDIST
Group
N
\(\overline{X }\)
ss
sd
t
p
GAIN
Control
32
10.43
9.26
62
− 19.715
0.000*
Intervention
32
54.15
8.45
*There is a significant difference at p < 0.05 level
As seen in Table 12, the results for the gain scores of both groups showed a statistically significant difference in favor of the intervention group in terms of graph interpretation skills (t = − 19.715, p < 0.05).
When evaluating the effect of laboratory implementation with sensors integrated with mobile technology on prospective teachers’ graph interpretation skills in terms of pre- and post-test scores of both groups, the situation is illustrated in Graph 2.
Graph 2
Comparison of pre- and post-test gain mean scores of graph interpretation skills
The findings regarding the current status of the graph drawing and interpretation skills of the intervention and control groups before the implementations, and the extent of change in favor of each group after the implementations, were examined. Based on the related graphs (Graphs 1 and 2), it is observed that the pre-test scores of both groups in the graph drawing and interpretation parts of the GDIST are equivalent. When Tables 6 and 10, which show the independent t-test results of the significance of the pre- and post-test scores of the GDIST between both groups, were analyzed, it was determined that there was no significant difference between the groups in terms of graph drawing and interpretation skills. Additionally, as a result of the dependent t-test for the pre-test and post-test scores of the control group, a statistically significant difference was found between the pre-test and post-test scores in favor of the post-test scores in terms of graph drawing and interpretation skills (Tables 7 and 11). In the same statistics for the intervention group, a statistically significant difference was found between the pre- and post-test scores in favor of the post-test scores in terms of graph drawing and interpretation. This finding indicates that both the intervention group, where mobile technology-integrated laboratory implementations were applied, and the control group, where classical laboratory tools and equipment were used, showed an increase in graph drawing and interpretation skills. However, when the gain scores of the control and intervention groups were examined using an independent t-test, a significant difference was found in favor of the intervention group (Tables 8 and 12). According to this finding, laboratory implementations integrated with mobile technology applied in the intervention group were more effective in enhancing prospective teachers’ graph drawing and interpretation skills compared to the control group, where classical laboratory tools and equipment were used.
It is observed that the skills of both groups, which were equivalent in terms of graph drawing and interpretation skills before the implementations, improved after the teaching. This improvement is attributed to the fact that both the intervention group, which used sensors integrated with mobile technology, and the control group, which used classical laboratory tools and equipment, followed the 5E learning model of constructivist theory. When examining the related literature, it is evident that implementations based on constructivist learning theory are effective in enhancing graph drawing and interpretation skills (Anagün & Yaşar, 2009; Iofciu et al., 2011; Taşdemir et al., 2005). This indicates that the present study aligns with the literature. In studies utilizing the 5E learning model, students are provided opportunities to plan and organize experiments, make observations, determine variables, record data, and create and interpret graphs, particularly during the discovery phase (Güngör Seyhan & Morgil, 2007). In the present study, it is believed that the effective use of the materials developed for both the intervention and control groups during the exploration phase, along with the preparation instructions, contributed to the improvement of prospective teachers’ graph drawing and interpretation skills. Additionally, the planning and organization of the experiments according to constructivist theory allowed prospective teachers sufficient time to create and interpret graphs after collecting data. In experiments and observations dominated by constructivist philosophy, it was found that prospective teachers interacted more with each other and complemented each other through discussions, thereby enhancing their graph drawing and interpretation skills.
As a result of the comparison of the gain scores of the intervention and control groups, a highly significant difference was found in favor of the intervention group. Both the control and intervention groups underwent a teaching process based on the 5E learning model of constructivist theory. The key difference distinguishing the intervention group from the control group was the use of sensors integrated with mobile technology within the 5E learning model. For students to understand the data presented in graphs, they need to know how the data is placed in the graphs. Therefore, teachers should provide opportunities for students in this regard. Additionally, to help students make sense of and interpret everyday life situations, emphasis should be placed on teaching the skills of graphing, data collection, and classification; establishing relationships between variables; and presenting these relationships through graphs (Çelik and Sağlam-Arslan, 2012). When examining the literature on the effect of technology on graph drawing and interpretation skills, many studies report similar findings (Anđić et al., 2020; Ersoy, 2004; Güngör Seyhan, 2022; Karatay et al., 2024; Okur, 2014; Simpson et al., 2006; Sönmez et al., 2005; Svec, 1999). Mobile technological devices equipped with special programs and integrated sensors can simultaneously convert sensitive experimental data into graphs, display the location of variables on the graph in coordinate systems, show their units, and present the distribution of data with equal intervals. Additionally, these devices allow for the inclusion of different variables, enabling the creation of graphs with various data sets.
The widespread use of computers has significantly changed the demand for and access to information. Similarly, the use of computers in education holds the potential to fundamentally transform how children learn graphic skills (Friel et al., 2001). Producing graphics in a computer environment does not require children to have the practical skills needed to create graphics or to be explicitly taught the rules of graphic creation. This allows students to focus on interpreting graphics, helping them achieve high levels of proficiency in interpretation skills (Ainley, 1995; Pratt, 1995). A study by Bryant and Somerville (1986) found that children aged six to eight did not find the spatial demands of plotting and reading points on pre-drawn line graphs difficult. Various studies by Phillips (1997) have examined young children’s graphic interpretation skills, including the use of motion sensors and other real-time data recording devices, and have provided evidence of “surprising proficiency” in some young students. Teachers and school mathematics programs emphasize both the traditions of graphical representation and proper presentation. Drawing neat and detailed graphics by hand is time-consuming for young children with limited motor skills. There is some evidence to suggest that children working on tasks that involve interpreting graphics in a computer environment gain important insights into the conventions and techniques of graphic creation (Ainley, 1995). The use of computers allows for the development of interpretation skills before the explicit teaching of practical skills in graphic drawing and the nuances and techniques of graphic creation. This provides an opportunity to reevaluate the traditional progression applied to the development of graphic creation skills (Ainley et al., 2000).
Recent research has shown that a laboratory education conducted with the help of technological tools that enable the drawing of real-time graphs integrated with sensors continuously improves students’ graphing skills (Adams & Shrum, 1990; Brasell, 1987; Linn et al., 1987; Mokros & Tinker, 1987; Svec, 1995). It can be argued that science education should occasionally be supported by these technological tools. To achieve this goal, teachers need to have sufficient training to integrate these tools into their teaching (Gado et al., 2006; Lyublinskaya & Zhou, 2008). Researchers examining the impact of such sensor-integrated, technology-supported experimental kits on the development of graphing skills have expanded their research on how these tools help students understand physical phenomena in laboratory lessons and improve their graphing skills. The study concluded that learning-teaching environments realized with such tools are effective in developing students’ graphing skills, that real-time graphs help students understand fundamental principles in laboratory lessons, and that they facilitate faster and more effective learning compared to traditional manual graphing methods (Linn et al., 1987). Researchers have noted that this type of teaching provides real-time data visualization, which offers immediate feedback to students and reduces cognitive load, thereby allowing them to analyze and interpret data more quickly. Another study investigating the impact of sensor-supported technological equipment on students’ graph interpretation skills and understanding of the concept of “motion” has shown that these tools are more effective than traditional laboratory methods in facilitating conceptual change. It was found that students’ graph interpretation skills significantly improved in these learning-teaching environments and that they better understood the concepts of “motion.” Furthermore, it was demonstrated that students developed their content knowledge specific to graphical problems and their graphing skills (Svec, 1995). In his study, Svec (1995) examined how learning-teaching environments utilizing these tools develop students’ graph interpretation skills within the framework of constructivist learning theory. Researchers emphasized that such environments provide students with real-time feedback and enhance their ability to formulate hypotheses by asking “what if” questions. This process enables students to gain a deeper understanding of scientific concepts and analyze experimental data more effectively.
Prospective teachers in the intervention group, who used mobile technology, were able to complete experiments more quickly than those in the control group. This allowed them more time to discuss the graphs and experiment results, focusing more on graph interpretation. This explains why the increase in graph drawing and interpretation skills was greater in the intervention group compared to the control group. The graphs obtained with these technological tools can be enlarged, analyzed regionally, and their slopes calculated, enabling prospective teachers to consider different dimensions of the graphs during interpretation (Güngör Seyhan & Okur, 2020; Okur, 2014).
Conclusions
This study was conducted to determine the effect of laboratory implementations with mobile technology for the “General Physics Laboratory-II” course on prospective science teachers’ graph drawing and interpretation skills.
It was concluded that the increase in the graph drawing and interpretation skills of both the students in the control group, where classical laboratory tools and equipment were designed and applied according to the 5E learning model, and the students in the intervention group, where mobile technology was integrated into the 5E learning model, was due to the effectiveness of the 5E learning model. This result shows that designing and implementing laboratory activities according to the 5E learning model is effective in increasing graph drawing and interpretation skills.
In the comparison of the gain scores of the prospective teachers in the control and intervention groups, it was concluded that the activities carried out with mobile technology were more successful in increasing graph drawing and interpretation skills.
As a result of the implementations carried out with mobile technological devices, it is believed that group work facilitated by these devices is useful for enabling discussions among group members in interpreting experiment results and graphs, as it shortens experiment time. However, since overcrowded groups can eliminate equal opportunities for using technological tools, it is suggested that care should be taken to ensure equal opportunities by not keeping the groups too large in studies where technological tools are used. Because the technological device is portable and its charge lasts a long time, it is recommended that measurements related to science and natural phenomena be carried out with sensors integrated with mobile technology, as this allows for non-laboratory activities to be conducted. In applications conducted with mobile technology-supported experimental kits, it is believed that the cognitive load on students is reduced. The real-time visualization of data during these applications accelerates the processes of analysis and interpretation for students. This immediate feedback mechanism not only allows students to quickly detect and correct their errors but also enables them to observe and analyze real-time data related to experiments. By altering variables and instantly observing the outcomes, students can develop a deeper understanding. This learning-teaching environment allows students to take an active role, enabling them to regulate their own learning processes and work independently. Students interacting with real-time graphs experience more meaningful learning by associating abstract concepts with concrete data. By manipulating data, they can personalize their learning experiences. This process facilitates interaction with dynamic rather than static data, which helps students better comprehend experimental data and develop their graphical interpretation skills. It enhances students’ problem-solving and critical thinking abilities. By asking their own “what if” questions, students formulate hypotheses and test them, thereby reinforcing their scientific thinking skills.
From this perspective, it is crucial to holistically incorporate graph literacy into school curricula. A review of the literature reveals that studies on science courses and graph literacy are predominantly conducted with formal education students, and the results indicate that students’ graph literacy skills are not at the desired level. According to Gültepe (2016), in everyday life, graph interpretation is an essential skill for all students, while graph drawing is not as widely needed. Individuals working and specializing in fields such as science, engineering, and academic research require this skill (Glazer, 2011). Leinhardt et al. (1990) discussed the difference between drawing and interpretation; interpretation involves responding to specific data (graphs, equations, data sets), whereas drawing entails creating new pieces that are not provided. Assessing the graph reading, drawing, and interpreting skills of prospective science teachers before they become active educators and creating learning-teaching environments to enhance these skills based on their current level is expected to contribute to the field. The earlier students develop graph literacy, the more advanced these skills can become. Therefore, it is essential to introduce graph literacy education at an early age to integrate it into scientific activities (Yalçın & Duran, 2022). The importance of graph interpretation should be emphasized, and more instructional and laboratory time should be allocated in science subjects such as physics, biology, and chemistry (Gültepe, 2016). By doing so, students can improve their graph interpretation skills, preventing mere memorization of formulas, and thus enhance their performance in predominantly numerical subjects. Students should be presented with data sets, asked to define variables, and interpret the general characteristics of graphs, or explore developments on an analytical plane. While implementing these activities, it is crucial to present and select graphs according to everyday life situations, facilitating the development of students’ graphing skills more effectively (Dugdale, 1993).
It is crucial for applications conducted with mobile technology-supported experimental kits to align with educational objectives and the curriculum. To achieve educational goals, it must be clearly defined how these technologies will serve the specific educational purposes outlined in the curriculum. This clarity ensures that teachers are more focused and goal-oriented when planning and implementing experiments. Adequate training is necessary for teachers to effectively utilize mobile technology -supported experimental kits. Continuous support should be provided to teachers, which may require additional time and resources. Assistance in resolving technical issues, teaching strategies, and integrating experiments can help teachers use technology more effectively. It is essential that these applications are designed to encourage active student participation. Students should be involved in collecting and analyzing data through hands-on experiments. The necessary technological infrastructure for these applications is also of paramount importance. Internet connectivity, computers, and other technological tools should be complete and functional. The maintenance and management of all equipment integrated with mobile technology used in these applications should be conducted regularly. Acquiring, maintaining, and updating such technologies may incur additional costs. In situations where applications conducted with mobile technological experimental kits cannot be executed in a real-time learning environment, the possibility that not all students have access to such technologies may negatively impact equality of educational opportunity. Considering all these critical details, the integration of these mobile technology-supported experimental kits into the educational process will be more successful and efficient. The effective use of these technologies is believed to increase students’ interest in science and offer more effective learning experiences. Acknowledging the disadvantages and challenges, educators and schools must make greater efforts to ensure that these technologies positively contribute to students’ learning experiences.
Declarations
Ethical Approval
The Ethical Review Committee of Sivas Cumhuriyet University has reviewed and confirmed that this research adhered to ethical standards and principals. If deemed necessary, a copy of the Impact Assessment Board Approval Report will be sent to you. The study conducted by the authors was carried out with the explicit consent of all participants involved, with both written and verbal information provided. We declare that all procedures performed in the study were conducted in accordance with the ethics committees and regulations.
Informed Consent
The informed consent letter outlines the specific nature of the study, including the data collected from participants. Participants were explicitly informed that this data is solely for research purposes and that their names would never appear in any collected data. Instead, their data was coded to ensure confidentiality and assigned unique identification numbers. Only the principal investigator of this study will have access to this information. Furthermore, any data that is no longer necessary will be securely destroyed. Participants were also briefed on how their participation would contribute to the goals of our study.
Consent to Participate
The participants involved in this research have been provided with comprehensive information regarding the potential risks and benefits associated with their participation. What is more, they have been reassured that any inquiries arising in the future will be addressed. They have willingly consented to partake in this study and acknowledge that this consent statement applies to each of them individually.
Consent for Publication
The authors will sign the consent to publish form if this manuscript is accepted by the journal for publication.
Conflict of interest
The authors declare no competing interests.
Limitations of the Study
One of the main limitations of this study was the relatively small sample size of the participants. This study anticipates that another research project with a larger sample size could yield different findings on the same topic. Future studies might consider conducting research with larger sample sizes in similar areas.
Research Involving Human Participants and/or Animals
Approval was obtained from the Ethics Committee of Sivas Cumhuriyet University. The procedures used in this study were conducted in accordance with the Guidelines of the Scientific Research Proposal Ethics Evaluation Committees at Sivas Cumhuriyet University. The procedures used in this study were conducted in accordance with the Guidelines for Ethical Review of Scientific Research Proposals established by the Higher Education Council for all higher education institutions.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
As the authors of this study, we declare that it is an original work that has not been previously published and is not currently under consideration for publication elsewhere.
