INTRODUCTION

Electrophoresis is a method for separating charged particles under an electric field. Electrophoresis, in its various forms or types, has become the most widely used method for analyzing biological molecules in biochemistry or molecular biology, including genetic components such as DNA or RNA, proteins, and Polysaccharides [1] [2]. The high-precision of electrophoresis has made it an important tool for advancing biotechnology [3]. Agarose gel electrophoresis is a form of electrophoresis used to separate DNA fragments based on their size [4]. Under the influence of an electric field, fragments will migrate to either the cathode or the anode, depending on the nature of their net charge. It is the most common means of separating moderate to large-sized nucleic acids and has a wide range of separations [5]. And an effective method for separating, identifying, and purifying 0.5 to 25 kb DNA fragments. It is known that the mobility is independent of the size of DNA with the size ~400 base pairs (bp) and larger, and it varies with the ionic strength of the electrolyte solution used [6]. The development of gel electrophoresis as a method for separating and analyzing DNA has been a driving force in the revolution of molecular biology over the past 20 years [7]. These techniques are now used by thousands of researchers and laboratory workers. More than half of all scientific papers published in biochemistry currently rely on electrophoresis methods [8]. In principle, understanding DNA gels conceptually is easy and technically feasible. In practice, many small details affect the accuracy and repeatability of the results [7]. The electrophoresis of Agarose gel is typically carried out using either Tris acetate EDTA (TAE) or Tris boric acid EDTA (TBE) buffers [9]. Research has identified other effective solutions compared to the mentioned buffers, with sodium bicarbonate being one of the most important due to its wider availability and much lower cost than other buffers [10]. Many researchers have studied alternatives to gel agarose that are less costly, these studies have included: gelatin, agar, and corn starch [11] [12] [13].The use and study of plant-based gel has not been sufficiently explored in previous research, so we focused on this type of gel in our study as it can be more easily and readily sourced than agar gel and is relatively cheaper. It can be concluded that the effective use of plant-based gel may lead to a wider range of electrophoresis application.

MATERIALS AND METHODS

Preparation of 1 L of 1X TAE buffer from 40X stock In DNA-related biological experiments, buffers are used to maintain a constant physiological pH. The electrophoretic mobility of DNA has been found to be strongly buffer dependent with TAE buffer pH for DNA fragments being ]15[ ]14[ 8.0. A volume of 50 mL of 40X TAE (Promega®) was measured into a 2 L beaker and was topped up with 1950 mL of distilled water to obtain a working solution of 1X TAE. Preparation of agarose gel (positive control) Agarose, a strongly gelling polysaccharide, is a common ingredient used to optimize the viscoelastic properties of a multitude of food products. This polymer is composed of a repeating disaccharide unit called agarobiose, which consists of galactose and 3,6-anhydrogalactose [16] [17]. The concentration of agarose in a gel depends on the size of the DNA fragments, which are separated with most gels ranging from %0.5 to ]19[ ]18[ %2. 1 g of electrophoresis-grade agarose (Vinantis®) was added to 100 ml of electrophoresis buffer. The gel was then prepared by melting the agarose in a microwave oven or autoclave and swirling to ensure even mixing. Melted agarose should be cooled to 50-60°C under running tap water before pouring it onto the gel cast. Gels are typically poured between 0.5 and 1 cm thick. The volume of the sample wells is determined by both the thickness of the gel and the size of the gel well [20]. Preparation of corn starch gel To prepare boric acid and sodium hydroxide buffers, corn starch was modified by adding amount of 1.855 g of boric acid and 0.48 g of NaOH, which were added to a 1 L beaker containing 200 ml of distilled water and stirred to homogeneity. We added 36 g of corn starch to the mixture and topped up with distilled water to the 1 L mark. The solution was stirred very well and allowed to stand in a water bath at 50 °C for 30 min. the supernatant was discarded, and the precipitant was kept. Next, 30 ml of distilled water was added to the precipitant and stirred to homogeneity, and then the Beecher was set in a water bath until it was dried. The modified dry starch was then ground until it became powder. An amount of 12 g of the modified corn starch was weighed and added to a beaker containing 100 ml of 1X TAE and the beaker was placed in a water bath until boiling. The supernatant was discarded, and the precipitant was taken and poured into the gel cast with the combs in place, and left until it solidified [13]. Preparation of Animal gelatin gel An amount of 1 g of animal gelatin powder was weighed and added to a beaker containing 100 ml of 1X TAE buffer. It was mixed well and microwaved for 2 minutes, with stopping every 30 s to gently mix it. The solution was cooled underwater. The gel was then poured into the gel cast. This protocol is commonly used in research. Preparation of an agar–animal gelatin gel mixture An amount of Agar (0.5 g) and animal gelatin (0.5 g) were weighed and added to a beaker containing 100 ml of 1X TAE buffer. The remaining steps are as mentioned in the animal gelatin gel. Preparation of %1 Agar gel An amount of 1 g of Agar was weighed and added to a beaker containing 100 ml of 1X TAE buffer. The remaining steps are identical to those for animal gelatin gel. Preparation of 1 L of sodium bicarbonate (SB) buffer Sodium borate is a Tris-free buffer with low conductivity. Therefore, gels can be run at higher voltages. SB produces sharp bands and nucleic acids can be purified for all downstream applications. However, SB is not as efficient as Tris-based buffers for resolution bands larger than 5 kb. Under standard electrophoretic conditions, SB provided resolution and separation as good as or better than TBE and TAE gels [21] [22]. A volume of 2 g of sodium bicarbonate, 0.12 g of NaOH and 0.05 g of NaCl was poured into a 1 L beaker and was topped up with 1 L of water to obtain a working solution of SB buffer [10]. Preparation of 1.5 % food grade agar-agar gelatin gel An amount of 3.75 g of food grade agar-agar gelatin powder was weighed and added to a beaker containing 250 mL of SB buffer. It was mixed well and microwaved for 2 minutes, with stopping every 30 seconds to gently mix it to avoid bubbles. The solution was cooled underwater. The gel was then poured into the gel cast. A 15 well comb was inserted, and the gel was left to solidify. The comb was gently removed, and the gel was placed in a horizontal gel tank. Sodium bicarbonate buffer was added to the gel tank at the maximum mark. Loading samples into modified corn starch %1 gel and agar – animal gelatin mixture %1 gel Loading ten microliters (10 µl) of human genomic samples [23] [24] after mixing them with 3 microliters (3 µl) of loading dye for all wells. The gel was placed in the tank containing 1X TAE buffer, passed through an electric current of 60 V for 5 minutes and then increased to 95 V for 1 hour. Afterward the gel was removed from the horizontal gel tank and dyed in Ethidium Bromide for 30 minutes because ethidium bromide (EtBr) is sometimes added to the running buffer during the separation of DNA fragments by agarose gel electrophoresis. It is used because when the molecule is bound to the DNA and exposed to a UV light source [25], Ethidium binds strongly to both DNA and RNA at sites that appear to be saturated when one drug molecule is bound for every 4 or 5 nucleotides [26]. It is then transferred to a tank of water with mild shaking for washing for 2 minutes. The gel was removed and viewed using a gel documentation device (UVP BioDoc-It). Loading Samples and Electrophoresis The DNA molecular weight standard control, also called the DNA marker (Ladder), the DNA ladder was separated by conventional agarose gel electrophoresis [27] [28]. Loading 10µL of human genomic samples after mixing them with 3µL of loading dye for wells 3 ,2 ,1 and 4, and 3µL of 50 bp DNA Ladder (vivantis®) in the fifth well. The electrophoresis involved the following steps: First, the voltage was 30 V for 5 minutes, the volt was increased to 45 V for 5 minutes, then to 60 V for 5 minutes, then to 70 V for 10 minutes, then to 90 V for 1 hour and a half (1.5 h). At the end, the voltage was increased to 95 V for 45 minutes in order to avoid DNA escaping from the wells. The gel was removed from the horizontal gel tank and dyed in Ethidium Bromide for 30 minutes and then transferred to a tank filled with washing water for 2 minutes. The gel was removed and viewed using a gel documentation device (UVP BioDoc-It). This protocol was performed for the %1 agarose gel and %1.5 treated food grade agar-agar gel.

RESULTS

Electrophoresis was performed for human genome samples, and a ladder of agarose gel with TAE solution was used as a control for the studied samples: Corn starch gel with TAE solution, agarose gel with TAE solution, animal gelatin with TAE solution, agar with TAE solution, agar – animal gelatin mixture with TAE solution, and food grade agar-agar gelatin with a solution of sodium bicarbonate sodium hydroxide and sodium chloride. All experiments were carried out under similar conditions of pH and using the same equipment and tools. Many aspects were compared during the experiment on a repetitive level, and a mean of duration of solidification, texture, color, dye duration and other parameters are mentioned in Table1.

From Figure 1 we find that the animal gelatin gel (c) forms a surface ice layer and is not fully hardened. One of the reasons for this is the very low heat and low concentration of animal gelatin. Corn starch gel (B) gave a white color and similar properties in terms of the structure with agarose gel. Other gels gave properties in structure and color very similar to agarose gel.

Before the samples were exposed to UV radiation, we can see from Figure 2 that the loading dye was electrophoresed for a distance of 1.2 cm in corn starch gel (A), for a distance of 1.7 cm in agar – gelatin mixture gel (B), for a distance of 0.9 cm for the loading dye and 1.3 cm for the Ladder in agar gel (C), for a distance of 1.4 cm for the loading dye and 1.7 cm for the Ladder in agarose gel (D), for a distance of 1.2 cm for the dye and 1.7 cm for the Ladder in treated food grade agar – agar gel (E) at the voltage and time shown in Table 1 for each gel mentioned. After the electrophoresis of 12% modified corn starch gel, 1% agar – animal gelatin mixture gel, 1% agar gel, 1% agarose gel, and 1.5 % of our treated food grade agar-agar gel with BS buffer, a separation of DNA was apparent, as shown in Figure 3, the modified Gel that is annotated with (E) in the Figure 3 shows good separation of the 50 bp DNA ladder (Vivantis®) in the 5th well, and the 4th well in E is genomic DNA extracted from human saliva. As for agarose gel D in Figure 3, good separation occurred in the 5th well and it showed resemblance to agarose gel in C. The gels were exposed to ultraviolet radiation and examined; we noticed that the disappearance of the fluorescence from the treated food grade agar-agar gel after 15 minutes, while the agarose retained its fluorescence for 25 minutes before the bands vanished from the UV. Knowing that the gels were dyed with the same type and concentration of dye and duration of time. For an electrophoresis buffer consisting of sodium bicarbonate, sodium hydroxide, and sodium chloride, it has shown high efficiency in securing the ions needed for electrophoresis while maintaining its physical and chemical properties; thus, it can be considered an equivalent solution to TAE solution. These results demonstrate the effectiveness of this variant using human genomic DNA. Electrophoresis with a ladder marker gave good results and good separation, as shown in Figure 4, which displays a comparison between agarose 1.5% and the treated food grade agar-agar gel 1.5%, where 5 µl of the marker was loaded into the wells in both gels at 40 V for 5 minutes and 80 V for 2 h followed by 30 minutes of soaking in an ethidium bromide tank. The results showed acceptable efficiency for the treated food-grade agar-agar similar to the efficiency of the agarose gel with some modifications in the method of work. Further enhancements of the images using gel documenting software could even make the separation look clearer for the treated food-grade agar -agar gel.

DISCUSSION

This research addressed finding a frugal alternative for agarose used in agarose gel DNA electrophoresis. The alternatives experimented in this research included: Agar, which originated in Japan in 1658. It was first introduced in the Far East and later in the rest of agarophyte seaweed-producing countries ^[29]. Agar is obtained from various genera and species of red–purple seaweeds—class Rhodophyceae—where it occurs as a structural carbohydrate in the cell walls and probably also plays a role in ion-exchange and dialysis processes ^[30]. Agar is a natural polymer commonly used in various fields of application, ranging from cosmetics to the food industry ^[31]. It is a gel forming polysaccharide with a main chain consisting of alternating 1,3-linked β-d-galactopyranose and 1,4-linked 3,6 anhydro-α-l-galactopyranose units ^[32]. Agarobiose is the basic disaccharide structural unit of all agar polysaccharides. Agar can be fractionated into two components: agarose and agaropectin ^[33]. The food-grade agar results were similar to agar results, yet microbiological agar can be more costly compared to food-grade agar-agar, and the treatment of agar with different salts described in this method gave slightly better results from previous research ^[12]. We also tested Starch, which is a major food source for humans. It is produced in seeds, rhizomes, roots, and tubers in the form of semi-crystalline granules with unique properties for each plant ^[34]. Edible and industrial corn starch was modified and used to prepare the electrophoresis gel. Corn starch is composed of two large α-linked glucose-containing polymers. Namely, smaller and nearly linear amylose and very large and highly branched amylopectin ^[35]. The starch alternative gel didn’t give good results and it was difficult to handle and too thick, so no DNA bands appeared ^[13]. Another alternative tested was Gelatin, which is a protein obtained by partial hydrolysis of collagen, which is the chief protein component in the skin, bones, hides, and white connective tissues of the animal body ^[36]. We can conclude from the gelatin gel result that it is not a good candidate for DNA gel electrophoresis, and this has been the case since the late 1980s.^[11] From the results shown in Fig. 1, we can conclude that modifying the materials concentration allows us to control the structural and solidification properties. It is important to consider the appropriate gel concentration for the gel’s retinal structure, which is where electrophoresis samples pass, and this is in accordance with Bertasa et al. 2020 research that describes a stronger gel formation and crosslinks with increasing the concentration and anhydro units in the gel in addition to alterations of appearance and color, yet this didn’t apply to gelatin and starch where gelatin lacked the strength to solidify and the starch was too thick and difficult to handle after pouring because it solidified very quickly. ^[37] We can observe that the previously mentioned gels in Fig. 2 resulted in the electrophoresis of dyes at least, and this is logical because these gels create a charge neutral trap for the negatively charged loading dye to pass through in the presence of an electric field and an electrolyte. ^[38] From our results shown in Table 1, treated food grade agar-agar gel prepared with sodium bicarbonate solution was the most closely related alternative to the commonly used agarose gel with modifications in the working method to achieve very close results with agarose, while starch gel failed to give a proper result. Also, the mixture didn’t give a clear result because the DNA samples couldn’t get out of the wells. Agar gel, the same as agar – agar gel, gave results similar to those of agarose. But still, treated agar – agar gel showed better results than agar gel by the distance crossed by the DNA samples and the display of the samples, in addition to the low cost. This result of genomic DNA electrophoresis is well known and is confirmed by several previous researches, such as Green et al. 2019, where large genomic DNA fragments migrate slower than smaller fragments, and smearing marks in the resulting gel image refer to poor-quality DNA or electrophoresis conditions. ^[39] As for cost, 1 g of agarose costs around 35,000 Syrian pounds, while 1 g of agarose costs almost 2000 Syrian pounds, while 1 L of TBE 1x costs nearly 700,000 Syrian pounds, while SB buffer roughly costs 3500 Syrian pounds for the same amount, this makes this alternative 15 times cheaper than agarose gel, and 200 times cheaper for the electrolyte used. It seems from Fig. 4 that the treated food grade agar-agar can show strong bands from the ladder, and the separation requires more time; hence, it could be recommended to use it for PCR products of one band and a ladder with several strong bands, and the background fluorescence from ethidium bromide on the treated gel could mean that there should be more rinsing time for it, in order to give clearer bands.

Conclusions and Recommendations

This study offers a very low-cost alternative to agarose gel to help laboratories with limited income. We found that treated food grade agar-agar could give similar results to those of agarose gel, and by using other buffers for electrolyte like SB buffer. Based on the results of this study, this method provides a low-cost alternative to agarose and TBE & TAE, and it can be used by low-budget labs with limited budgets to make DNA assays more domesticated, where the alternatives suggested here cost 15 times less than the industrial agarose and electrolytes. Further research is recommended to enhance the clarity of the gel and explore the potential applications of this new gel in RNA separation and plasmid DNA separation and PCR amplicons of different lengths.

INTRODUCTION

Our modern age is characterized by rapid and tremendous development in science and technology. Each learner has a smartphone and accessing the internet has become a daily need, a habit for some of us, and a source of income for others. And the development of (5G) networks that revolutionized technology and social networking for people and devices “Internet of Things”. This has led to the imposition of modern requirements to prepare the individual to keep pace with the developments of this era in all the fields related to our lives. One of the most important fields is education, especially in mathematics because of its importance in the different fields of life, computer science and especially algorithms. With the advent of technology, mathematical technologies appeared in education and proved their feasibility; the use of technological innovations in teaching math prepares learners for a High-Tech centric world and develops higher mental cognitive skills, such as problem-solving, thinking, data collecting, analysis and proof. Which fall within the scope of creativity and invention [1]. Fields of mathematical technology have diversified following the technological development of computers, mobile phones and the software used in them in addition to other technologies such as interactive whiteboards, the spread of the Internet and the educational services and platforms it provides. Mathematicians were able to use all these technologies in teaching mathematics. The benefits of mathematical technology are not just for students, it has an impact on teachers as it supports the creativity of teachers as learners and task designers and provides the opportunity to develop many new mathematical meanings [2]. Several scholars have investigated the technological pedagogical content knowledge (TPACK) for math teachers. Alternatively, a subset of them: Mailizar and Fan (2019) investigated Indonesian math teachers’ technological pedagogical content knowledge. The study used a questionnaire, and the sample consisted of (341) math teachers. The results showed that the understanding of mathematical technology ranked low and suggested more training courses for teachers [3]. In Malaysia, Bakar, Maat and Rosli’s (2020) study aimed to determine the math teacher’s self-efficacy in integrating technology and (TPACK). The study used a questionnaire containing (71) items, and the sample consisted of (66) national secondary math teachers. The results showed no gender or educational experience differences [4]. In Kenya, Mukenya, Martin and Shikuku (2020) investigated the knowledge and skills of math teachers to integrate ICT into secondary school education. The study used a questionnaire, and the sample consisted of (218) math teachers and heads of departments. The results indicated that teachers need more knowledge and skills to use ICT. They suggested that the Ministry of Education should work on policies to develop teachers’ ICT pedagogy and review the curriculum [5]. In Spain, the study of Gómez-García, Hossein-Mohand, Trujillo-Torres and Hossein-Mohand (2020) investigated the training and use of ICT in teaching mathematical concepts. The study used a questionnaire, and the sample consisted of (73) high school math teachers. The results showed differences in favor of teachers with less education experience and no gender differences [6]. Spangenberg and De Freitas (2019) in South Africa investigated the levels of (TPACK) and ICT integration barriers. The study used a quantitative questionnaire, and the sample consisted of (93) math teachers. The results showed poor technological content knowledge and suggested continuous professional development programs for teachers in specific ICT integration [7]. In Turkey, the study by Ozudogru and Ozudogru (2019) investigated math teachers’ technological pedagogical content knowledge. The study used a questionnaire containing (39) items, and the sample consisted of (202) math teachers. The technological knowledge section results showed significant differences in gender in favor of males and no differences in teaching experiences or school level [8]. In addition, the study of Birgin, Uzun and Akar (2020) investigated Turkish mathematicians’ perceptions of their proficiency in using ICT in teaching. The study used a descriptive survey; the sample consisted of (242) math teachers. The results showed that teachers’ knowledge of mathematical software is low, and there are no gender differences. However, there are differences in favor of teachers with less experience in education in terms of efficiency [9]. In China, Tan and Jiang (2021) aimed at the mathematical technological knowledge of elementary school math teachers. The study adopted the qualitative paradigm and a sample of (24) math teachers. The results showed that the teacher’s knowledge and use of technology classification are good. The previous research has yet to study the relationship between teachers’ knowledge and teachers’ training courses, academic qualifications, and teachers’ Internet access. Accordingly, this study will contribute to bridging this research gap.

Technological Mathematical Knowledge (TMK)

In 1986, Shulman came out with the (Pedagogical Content Knowledge) framework, which teachers need in terms of knowledge and tools to teach specific content. He considers educational technology a tool that facilitates teaching [11]. After the advent of E-learning and E-class design, Kohler and Mishra 2006 added technology as an independent regard of knowledge and not as a helping tool for teaching; (Technology knowledge) is the knowledge of technologies involving the skills of operating and using the old and new of them [12]. Also, they define the concept of (Technological Content Knowledge) as “an understanding of how teaching and learning can change when particular technologies are used in particular ways.” [13, p 65]. Thus, Schulman’s framework was expanded to (Technological Pedagogical Content Knowledge), which aims to demonstrate the necessary competencies for teachers to integrate technology with education [12]. Koehler and Mishra (2009) have embodied the framework in the “What is TPACK” study. The framework was a schematic illustrating the intersection of the three pieces of knowledge within the framework and the new knowledge resulting from its meeting with seven pieces of knowledge. As a result of the development of educational sciences and technologies, researchers [10,14,15] customized the content in the (TPACK) framework to include only mathematical content. [14] developed the Technological Pedagogical Mathematical Knowledge (TPMK) concept. [15, p 1] used the (Mathematical Technological Knowledge) concept, which they define as a “teacher’s knowledge of the technology developed as a result of exploring mathematics with technology”. This concept has an issue because some technologies are not just mathematical like an interactive whiteboard or Google apps. Similarly, [16, p 342] used the (Technological Mathematical Knowledge) concept, which they define as “the teacher’s knowledge of technological tools that can be used to represent mathematical knowledge”. However, [3, p 5] defines the broader concept of ICT-content knowledge as “knowing how to use ICT to represent, communicate, solve and explore mathematical contents, ideas, or problems without consideration of teaching approaches”. Taking advantage of these definitions, this paper defines (Technological Mathematical Knowledge) as knowledge of educational technologies hardware- and software along with how to use them to represent, explain, solve and explore mathematical content, ideas or issues regardless of the educational pedagogy, ” how to make a circle within a triangle using GeoGebra” [16, p 2].

Educational Technologies for Mathematics

Interactive Whiteboards

An interactive whiteboard is a versatile tool that allows teachers to deliver engaging lessons using various applications and educational programs [17]. Studies show it improves students’ math achievement [18]. And can benefit displaced learners in challenging environments.

Computer Algebra Systems: One of the most prominent software applications is GeoGebra. It can solve quadratic equations by graphing and accurately representing geometric transformations, statistical representation and data analysis, providing an interactive geometric environment for learners and representing shapes with a 3D environment; meanly, learning by GeoGebra improves the geometrical abilities of students [19]. In addition to its positive impact on achievement [20], it is also one of the best technological options that enriches the quality of research and mathematical conception from different perspectives that support feedback. It also provides strategies for teachers to teach according to students’ needs and facilitates learning through virtual representations that represent reality and focus on educational benefits [21]. Thus, the use of GeoGebra has a significant impact on mathematical abilities [22]. Another example is Sketchpad which combines geometry designs with algebra and calculus, curves representing descendants, then algebraic representation such as coordinates or equations and finally, a data table representation [23]. Sketchpad shares the advantage of learning through practice and developing the learner’s ability to use these applications with GeoGebra on smartphones [24].

Coding language: Scratch, for example, is a straightforward and exciting initial learning tool for understanding basic programming principles, creating educational and recreational content, building mathematical and scientific projects and simulating and visualizing experiments. Scratch not only allows learning math in an easy, effective and exciting way, but teachers also use it to teach basic mathematical principles of arithmetic and geometry [25]. In short, scratch is superior to other programming languages by attracting children to learn programming in the future [26].

Smartphone apps: are a form of distance learning and an extension of E-learning. Teachers can provide math content and follow learners anywhere, anytime by designing high-quality digital learning objects in math. Students can also learn mathematical content according to their circumstances and needs [27]. Moreover, the smartphone was the best technology for teachers during the COVID-19 pandemic [28]. It also supports applications such as Kahoot, a free educational program that supports many languages, such as Arabic, based on the play-and-response classroom system. It also helps students learn and self-evaluate, better demonstrate what they have learned, make math more exciting and vital and increase motivation to learn [29].

Online Tools: The field of education has been revolutionized by two powerful types of tools. The first type is the learning management system, such as MOODLE, an open-source program utilized in over 235 countries to support the E-learning process. Particularly effective in math education, MOODLE encourages learners to engage in cognitive thinking skills and fosters the generation of new ideas [30]. The second type is online learning resources, including Massive Open Online Courses (MOOCs), which cater to both teachers and students. These resources that are available through platforms like Coursera, Alison, Udemy and others, offer high-quality content in various specialties such as mathematics, computer science and languages. MOOCs have proven to be an invaluable resource, helping teachers enhance their professional knowledge and enabling students to access a wide range of courses, including specific mathematics courses [31], through platforms like Coursera, EdX and others. These platforms provide videos that can effectively supplement classroom learning, allowing teachers to explain complex concepts more easily.

The war in Syria had a significant impact on the education sector; it destroyed schools and displaced students, which led teachers to adopt unconventional education methods even before the COVID-19 pandemic, which was the first real challenge to educational technologies. According to McGonigal (2005) as cited in [32, p 49]“Teachers need an activating event to expose the limitations of their current knowledge”. So, what event is more challenging than war or a pandemic?

This phenomenon raises a controversial issue; did teachers have the knowledge and skills to help them cope with this crisis? And how did their knowledge and skills develop after the crisis? The current study aims to classify the technological mathematical understanding of Syria’s math teachers and the effects of demographic variables. Consequently, two questions and five related null hypotheses were formed for demographic variables, as follows:

• What is the classification of Syrian math teachers’ technological mathematical knowledge?
• Are there statistically significant differences in teachers’ technological mathematical knowledge according to gender, academic qualification, years of experience, training courses, and Internet access?

METHODS

Participants

The online survey was shared in a Facebook group for Syrian math teachers. The researcher used the approval of the Ethics Committee of the Ministry of Education. Data was collected in the second semester of the 2021-2022 academic year. The sample was limited to (219) teachers, as shown in Table 1.

Tools

The study used a questionnaire based on [3]. The validity of the study tool was confirmed using an independent T-test and the reliability was assessed with a Cronbach-Alpha coefficient value of 0.859. Its items were classified into two parts; the first included demographic information, including gender, academic qualification, years of experience, established courses and Internet access. Part two: aimed at Technological Mathematical Knowledge, consists of (3) items intended for knowledge of educational devices, (4) items aimed at general understanding of software, (4) items aimed at knowledge of computer mathematical software, (4) items aimed at knowledge of Smartphone tools, two items on knowledge of online tools, (7) items aimed at mathematical technology content knowledge at levels:( strongly disagree, disagree, neutral, agree, strongly agree)

Data Analysis

In this study, the researcher used SPSS for statistical analysis, including coding responses into a five-point scale, calculating averages and standard deviations, conducting T-tests for validity, gender, and internet access, applying Cronbach’s alpha for reliability, using ANOVA for comparing mean responses in the case of (Academic qualification, courses, and Years of experience), and performing Fisher’s LSD test. All hypotheses were tested at a significance level of α=0.05.

RESULTS

Technological Mathematical Knowledge (TMK) of Syrian Math Teachers

Table (2) shows that the mean score of teachers’ knowledge of hardware was (3.37), which is higher than the average. In addition, their mobile knowledge was higher than their computer and interactive whiteboard knowledge, and the mean score of teachers’ knowledge of general software was (3.14), and the table shows that knowledge of Microsoft applications was the highest with average (3.92), the average knowledge of mathematical software was (2.53) which is below average, dynamic applications such as GeoGebra appear as the highest mean (2.84), the mean score of mobile tools was (3.47), which is higher than the average, and social media apps show the highest mean score (3.84), the mean score of online tools was (2.70), but the mean score of using mathematical technology was (2.30), which is below the average, and the highest field of use was in geometry with an average of 2.41.

The Effects of Demographic Variables on Technological Mathematical Knowledge.

Gender differences in teachers’ (TMK)

Table 3 shows the results of an independent sample t-test comparing the means of teachers’ technological mathematical knowledge based on gender. The table shows that the mean score for male teachers is 3.13 and the mean score for female teachers is 2.75, the t-value is 3.922. A higher t-value indicates a larger difference between the means, the significance level of less than 0.05 is typically considered statistically significant. In this case, the significance level is 0.000, which is less than 0.05. Based on the t-test results, we can reject the null hypothesis that there is no difference between the means of technological mathematical knowledge scores for male and female teachers. So, there is a statistically significant difference between the means, with male teachers scoring higher on average than female teachers.

Academic qualification differences in teachers’ (TMK)

The results of the one-way ANOVA analysis in Table 4 indicate a statistically significant difference (p < 0.05) in technological Mathematical Knowledge scores between teachers with different academic qualifications. This means that we can reject the null hypothesis that there is no difference in scores between the groups.

Further analysis using the LSD test in Table 5 helps pinpoint which specific groups differ from each other. The LSD test reveals significant differences in technological proficiency scores between the following groups:

• Diploma and bachelor’s degree holders (average difference: -0.25268 & Sig = 0.193)
• Diploma and master’s degree holders (average difference: -0.5207 & Sig = 0.015)
• Master’s and bachelor’s degree holders (average difference: 0.2680 & Sig = 0.028)

the researcher concludes that there are statistically significant differences in teachers’ (TMK) based on academic qualification in favor of the master’s degree group. At the same time, there were no differences between the bachelor and diploma groups.

Training courses differences in teachers’ (TMK)

The results of the one-way ANOVA analysis in Table 6 indicate a statistically significant difference (p < 0.05) in Technological Mathematical Knowledge scores between teachers with different training courses. This means that we can reject the null hypothesis.

The LSD test in Table 7 reveals significant differences in technological proficiency scores between the following groups:

• No Courses and Technology Integration Courses (average difference: -0.09665& Sig = 0.370)
• No Courses and MOOCs (average difference: -0.50209& Sig =0.001)
• Technology Integration Courses and MOOCs (average difference: -0.40554& Sig = 0.006)

the researcher concludes that there are statistically significant differences in teachers’ (TMK) based on Training courses in favor of the MOOCs group. At the same time, there were no differences between the No Courses and Technology Integration Courses groups.

Years of experience differences in teachers’ (TMK)

The results of the one-way ANOVA analysis in Table 8 indicate a statistically significant difference (p < 0.05) in Technological Mathematical Knowledge scores between teachers with different Years of experience. This means that we can reject the null hypothesis.

The LSD test in Table 9 reveals significant differences in technological proficiency scores between the following groups:

• 1-7 years and 8-14 years (average difference: 0.48341& Sig = 0.007)
• 1-7 years and 15 years and more (average difference: 0.39751& Sig = 0.002)
• 8-14 years and 15 years and more (average difference: -0.68713& Sig = 0.280)

The researcher concludes that there are statistically significant differences in teachers’ (TMK) based on Years of experience in favor of the 1-7 years group. At the same time, there were no differences between the -14 years and 15 years and more groups.

Internet access differences in teachers’ (TMK)

Table 10 shows the results of an independent samples t-test comparing the means of teachers’ technological mathematical knowledge based on Internet access. The mean score for 3G/4G Network is 2.43 the mean score for ADSL Network is 3.85, t-value is 3.734 & (p = 0.000 < 0.05). Based on the t-test results, we can reject the null hypothesis. So, there is a statistically significant difference between the means in favor of the ADSL Network.

DISCUSSION

Results of the study showed that the general knowledge about devices was slightly above average and that a higher percentage of math teachers used smartphones because it is easy to use and widely available among learners in WhatsApp and Facebook groups as indicated in [9]. This result contradicts [3], where the highest percentage was computers. However, the researcher added an interactive whiteboard instead of the graphing calculator in our study. Our study indicates that Syrian math teachers’ general software knowledge ranked slightly above average. The highest percentage was Microsoft applications because it is familiar and easy to use and its training courses are easily accessible (ICDL). In this section, our findings are consistent with the results of [3, 9, 5], and add a section for smartphone applications, consistent with [33] in the excellent degree of using the WhatsApp application. Within the knowledge of mathematical software, the highest percentage was for GeoGebra. The reason might be to support the Arabic language and for its easy-to-use qualities. Besides, smartphone applications were more elevated than computer applications. As for Internet tools, knowledge of learning resources such as Coursera was higher than knowledge of learning management systems. This result contradicts [3] during a pre-COVID-19. This difference indicates that teachers use smartphones directly as an educational tool or a learning resource in times of crisis. The results showed poor use of mathematical technologies; a possible explanation might be that teachers are not well qualified for these technologies and not good enough at English. Another possible explanation is that most educational technological devices are unavailable in schools because the Ministry does not provide schools with such devices, which may be due to their high cost and the difficulty of producing them locally, along with the circumstances of war. This conclusion supports [5], which linked poor knowledge and use to the unavailability of technologies and devices in schools. On the other hand, [10] ranked the expertise and use of technology by Chinese math teachers as good and the integration of technology with education as excellent, owing to the availability of devices in Chinese schools.

Gender: There were significant differences in teachers’ (TMK) based on gender in favor of the male group, and this might be due to female teachers being busy with their household duties, so they do not have time to learn or use modern technological skills, unlike male teachers who have time to learn and use new technologies. This conclusion supports [8], which explains that male students tend to be more technological than female students who want to study languages and social sciences. This result is contrary to [9, 34, 6] where they showed no gender differences.

Academic qualification: There were significant differences in teachers’ (TMK) based on academic qualification in favor of the master’s degree group; a possible explanation is that master’s degree holders have excellent English and research skills. Besides, a good relationship with the Internet and all the new technologies in their specialties. As Patalinghug and Arnado [35, p 585] have pointed out “It would be a good practice for teachers to pursue advanced degrees like master’s degrees or even higher degrees” unlike the teachers who stopped at the bachelor’s or diploma, as they do not require development or scientific research. He satisfied himself with his job as a middle or secondary teacher, which does not require technical skills in our schools, [36] recommended a bachelor’s degree program should be redefined with smart technologies so students can learn fast and subjectively. Teachers might also need more time to master new technology. As [37, p 9] has mentioned, “Teachers may also feel that they do not have the time to learn new technologies because there have been many changes to middle and high school math courses and curriculum over the past several years”.

Training courses: There were significant differences in teachers’ (TMK) based on training courses in favor of the MOOCs group. This result might be because mathematical technologies are still new; therefore, they need advanced techniques that are not available in the ministerial integration courses. Logically this result supports the impact of MOOCs on teachers’ professional development and technological skills, as the studies of [38, 39] have indicated. In the USA, researchers have tested MOOCs as a teacher training course that provides content-focused experiences using technology. Expert trainers successfully designed exciting experiences for teachers that positively affected their perspectives, practices and beliefs in math teaching and statistics [39]. MOOCs worldwide allow teachers to forge partnerships and create learning communities that improve their professional knowledge and skills [40].

Years of experience: There were significant differences in teachers’ (TMK) based on years of experience in favor of the ‘1-7 years’ group. These teachers started their careers in the harshest circumstances of the war and then the COVID-19 pandemic. So, this shows that they were more resilient to learning modern technologies that helped them overcome these conditions. This result is consistent with [6], which explains that teachers with less education experience have better training in ICT and use it broadly. However, this result contradicts [8, 34], where they showed no years of experience differences.

Internet access: There were significant differences in teachers’ technological mathematical knowledge based on Internet access in favor of (ADSL); a possible explanation is that (ADSL) is more stable and cheaper in developing countries like Syria. Therefore, it allows teachers to comfortably explore the Internet, enroll in any course, such as a course on Coursera, and watch a large number of instructional videos on YouTube, unlike the limited access (3G/4G).

CONCLUSION AND RECOMMENDATIONS

The present research aimed to classify the technological mathematical knowledge of Syrian math teachers. The results showed that its classification is below average, with the highest percentage of smartphones and their mathematical applications. In the face of unprecedented challenges like war and pandemics, teachers must remain committed to developing themselves and their skills. Our research reveals a powerful tool for overcoming these obstacles: a strong relationship with the internet. By leveraging the vast resources available online, teachers can advance their mathematical and technological knowledge and equip themselves to better serve their students. This is a critical time for educators to embrace the power of technology and chart a path forward to a brighter future. This paper suggests that Ministries of Education develop comprehensive teacher training programs to prepare teachers for crises like war or pandemics. These programs should focus on developing teachers’ skills in modern mathematical software tools, mathematical applications, social media platforms, distance learning platforms, interactive lessons and E-testing. They can be extended to cover other educational subjects and mathematical technologies should be introduced to build the technological mathematical knowledge of graduates. Finally, teachers’ access to the Internet must be supported. These measures will ensure quality education during crises.