445
Digital Map Media for Teaching
Subtopic Tsunami Disaster and Mitigation
Teuku Hasan Basri
1, Rachmatsyah
2, Cindi Rasta Br Ginting
3, Saiful Anwar Matondang
41,2,3Universitas Samudra Aceh
4Universitas Islam Sumatera Utara
ARTICLE INFO ABSTRACT
Article History:
Accepted: 05-07-2022 Approved: 18-10-2022
Abstract: The goal of this study was to identify the factors that affect geography students' abilities to use digital tsunami-based maps for disaster mitigation. Multiple linear regression analysis is used in this work. The findings demonstrated that students' learning outcomes in the disaster mitigation sub-materials were significantly impacted by the usage of digital maps in geography instruction. The skills of lecturers, student responses to utilizing tsunami-based digital maps as learning resources, and classroom learning activities between lecturers and students all have an impact on how well students can utilize tsunami-based digital maps. Thus, it can be said that digital maps based on tsunamis are helpful in teaching pupils about disaster preparedness.
Keywords:
digital maps;
educational media;
mitigation
Correspondence Address:
Saiful Anwar Matondang Universitas Islam Sumatera Utara E-mail: [email protected]
Disasters are unpredictable events that cannot be foreseen by any human being. Nevertheless, disasters may be mitigated using numerous scientific techniques that have been researched. So many natural disasters strike Indonesia's many areas, causing a wide range of disasters of varying scale and frequency. Natural disasters create large losses, either directly or indirectly, such as loss of life, property destruction, infrastructure damage, a harmed environment, and trauma for survivors, and are triggered by conflicts in human relationships or activities with other people, such as tribal or group disagreements. The issuance of Law Number 23 of 2014 concerning Regional Government regulates disaster management to become a mandatory affair. This policy is then expected to strengthen the institutional capacity of disaster management in the regions. Efforts to reduce the disaster risk index will be able to be carried out in the regions by implementing the planning, implementation and monitoring and evaluation phases. The authority of the regional government in implementing programs and activities related to disasters can be implemented across sectors, through the implementation of plans based on coordination at the regional level. Technological advances make academics, and practitioners continue to develop discoveries in predicting disasters that will arise from various regions to monitor disasters that will come in the future. So in this case the development of digital media can also be developed by various scientists for the smooth prediction of natural disasters that will occur at any time in various regions.
With digital as it is today, it also requires cadets to develop a digital map media so that it is easy to see in dealing with disasters such as determining disaster-prone points in the area, especially Langsa City, which has five sub-districts including Langsa City District, Langsa District. East, West Langsa District, Langsa Baro District, and Langsa Lama District. From these five sub-districts, several villages are prone to disasters in Langsa City including, Sidorjo Village, Sidodadi Village, Lengkong Village, Karang Anyer Village, Meurandeh Village, Teungoh Village, Java Village, Seulalah Village, Bayeun Village, Kuala Village Langsa, Alue Beurawe Village, and Sungai Pauh Village. so in terms of developing science in this digital era, it also requires scientists to develop digital maps to determine disaster-prone points that occur in Langsa City which are useful for the community, students and educators to see areas prone to disasters in the Langsa City area.
Natural disasters are a major issue that cannot be avoided. Various catastrophes strike humanity every year, even every day, causing a variety of effects such as material losses, mental problems, environmental harm, and diminished communal welfare (Makwana, 2019). According to the UN International Strategy for Disaster Reduction, Natural and technical catastrophes (Faivre et al., 2018). Hydro-meteorological disasters (floods, hurricanes, droughts, and landslides), geophysical disasters (earthquakes, tsunamis, and volcanic activity), and biological disasters are the three types of natural disasters (epidemics, plant and animal diseases). Technical catastrophes may be classified into three categories: industrial accidents (such as chemical spills, industrial infrastructure damage from gas leaks, radiation, and other events), transportation problems (such as train and plane crashes), and other occurrences (domestic or non-industrial structures, explosions, and fire) (Chaudhary &
Piracha, 2021; Islam et al., 2021; Vieira & Anthony, 2021).
DOAJ-SHERPA/RoMEO-Google Scholar-IPI Halaman: 445—451
An earthquake occurs when rock strata in the earth's crust shift suddenly owing to the movement of tectonic plates, causing the earth to shake (Putra et al., 2021). The following are some of the possible causes of earthquakes: (1) tectonic movement (2) volcanic activity (3) meteors (4) avalanches (5) nuclear explosions, (6) below-the-surface avalanches. Tectonic earthquakes are earthquakes that occur as a result of tectonic plate movement activity (He et al., 2021). Also known as volcanic earthquakes, earthquakes can occur as a result of volcanic activity. Energy is released in the form of earthquake waves or seismic waves when rock strata in the ground shift suddenly.
Earthquakes are the most feared disaster for the Indonesian people; an average of 6000 earthquakes with various impacts occur every year. A tsunami is one of the effects of an earthquake; they often cause damage in the territory of Indonesia and even casualties, as can be seen from the tsunami caused by the earthquake in Aceh in 2004, the tsunami caused by the earthquake in the Mentawai Islands in 2010, and others (Rasyif et al., 2020). A Tsunamis can be caused by (i) earthquakes with a fairly large vertical motion that occur under the sea, (ii) volcanic eruptions under the sea, and (iii) avalanches that occur under the sea (Casalbore et al., 2020; Selva et al., 2021). It's important to remember that not all little earthquakes result in major ones, and that seismic waves can cause days' worth of Earth-shaking activity. The majority of earthquakes occur less than 80 km under the Earth's surface, and the frequency of most earthquake waves is less than 20 Hz, thus humans can only hear the sound of items being shaken. This phenomenon is known as earth-free oscillation.
Disaster mitigation is a continuing endeavor to lessen the effects that disasters have on people and property (Ji & Lee, 2021). A few examples of mitigation strategies are keeping residences away from floodplains; constructing bridges that can withstand earthquakes; creating and enforcing construction codes that can withstand hurricanes; and more (González et al., 2020). The concept of mitigation is "consistent action that reduces or eliminates long-term threats to people and property from natural catastrophes and their repercussions" (Godfrey et al., 2019). The continual endeavor to decrease the effects of disasters on our families, homes, communities, and economy is described at the federal, state, local, and individual levels. Due to the disaster's disproportionate negative effects on the already vulnerable and the absolutely shocking loss of life, illnesses, injuries, psychological effects, displacement from one's home and community, social and financial consequences, and other losses, it is crucial to fully implement the best disaster mitigation principles and practices (Griego et al., 2020).
Disaster mitigation is a key element in the disaster management process because it has the potential to protect people from potential fall casualties, property damage, and physical or social vulnerabilities by implementing or improving positive mitigation activities. Mitigation is an activity to minimize earthquake and tsunami victims. Predicting earthquakes, pre- earthquakes, pre-temporary, and post-earthquake or tsunami activities is part of mitigation. There are two ways to anticipate earthquakes: (i) short-range prediction; and (ii) long-range prediction. Arends (1997) examined a "Mitigation Socialization Model for Disaster Prone Community in West Java" as one of many disaster mitigation studies. Since natural disasters occur once a year and the epicentrum is close by, it was reported that West Java is particularly vulnerable to them. There are many people of working age living in the neighborhood, which has a high population density. In other words, there is a lot of dependency in West Java. Mobility and construction quality are poor. The inhabitants know very little about preparing for disasters. Because of poverty, a lack of knowledge, and limited access to technology, the survival rate is lower. According to Arief Budiman's research, Indonesia is vulnerable to earthquakes like those that struck Yogyakarta and Aceh a few years ago. A mobile phone application with information on earthquake mitigation was created as part of this project (Arief, 2012). According to the theoretical framework discussed above, mitigation is the first step in lowering the risk of disasters. Given that disasters can occur suddenly, it is crucial to acquire knowledge about disaster mitigation.
Digital maps are saved depictions of geographic phenomena that a computer can display and study. Digital maps can also be thought of as a scaled-down depiction of the surface of the world that is presented digitally and reduced using a projection technique (Varanka & Usery, 2018). Unlike traditional maps that are printed on a flat surface, digital maps are displayed differently. Digital maps typically have a large file size and a particular format that a computer can process A digital map stores each object as a single or set of coordinates (Guth et al., 2021). One type of object will be recorded as a coordinate, such as the location of a point, whereas another type of object, such as an area, will be saved as a set of coordinates. When compared to analog maps, digital maps have a number of benefits, including the following: (a) They have consistent quality;
unlike paper, which can be folded, expanded, or torn when stored, digital maps can always be restored to their original form without losing any quality (Minasny et al., 2019); (b) they are easy to store and transfer from one storage medium to another.
Analog maps stored on paper rolls, for example, take up more space than digital versions stored on a hard drive, CD-ROM, or DVD-ROM; (c) digital maps are easier to update. Using certain software might make editing easier when it comes to updating data or changing the coordinate system (Liu et al., 2018). Digital mapping is, in theory, the process of gathering data in the form of digital photographs. Its primary purpose is to produce maps that accurately reflect a certain area. Initially, digital maps served the same basic purpose as analog ones: they offered a simulated representation of the main road as it was delineated by the terrain in the vicinity. Digital maps can be used for many purposes, including locating areas of interest, mapping electricity flow information, and adding location services to make them more user aware. The 21st century is a digital age where all access is dependent on technology connected to an internet network. Hence, teachers must be able to use technology as a medium of
learning, like digital maps in the classroom. Digital maps are used in the learning process to facilitate student comprehension of the subject matter, enhance student learning outcomes, boost learner motivation, and assist teachers in distributing course materials.
METHOD
Data on the advantages of utilizing a digital map to teach about the tsunami disaster was gathered using a qualitative research design. Distribution of questionnaires and instrument-aided observation were the methods used to obtain the data. It took place at Universitas Samudra in the province of Langsa Aceh. Everyone who was a part of Universitas Samudra's administration and learning process made up the population. The samples included the head of the geography study program at Universitas Samudra and 39 geography students who were enrolled in disaster prevention classes and lectures. Multiple linear analysis techniques and the SPSS program were employed to determine the impact of the lecturer's competence, lecturer and student activities in the classroom, student replies, and learning quality. The analysis formula was as follows:
ŷ =a + b1X1 + b2X2 + b3X3
Ŷ : quality of learning outcome a : constant
b1 : lecturer’s ability regression coefficient
b2 : lecturer’s and student’s activity in the classroom regression coefficient b3 : student’s response regression coefficient
X1 : lecturer’s ability
X2 : lecturer’s and student’s activity in the classroom X3 : student’s response
e : Standard error
Coefficient of Determination Analysis
The coefficient of determination assessed how well a model can account for the variance in the dependent variable (Ghozali, 2011). The following formula is used to calculate the coefficient of determination, which is used to calculate the proportion of the quality of learning outcome that is affected by the lecturer's skill, the activity of the lecturer and students in the classroom, and the response of the students.
R2 = r2 x 100%
Where:
R2 : coefficient of determination r : coefficient of correlation
The coefficient of determination is between zero and one. Lower R2 means independent variables have very limited ability to explain the dependent variable but when R2 is closer to one, independent variables can provide nearly all information to predict the dependent variable.
RESULTS
Multiple Linear Regression Analysis
Multiple linear regression was used to assess the relative contributions of the lecturer's expertise, the students' involvement in class, and the students' perceptions of the caliber of learning results. Table 1 displays the results of the multiple linear regression.
Table 1. Output of Multiple Linear Regression
Variable Coefficient Beta T Sig t Result
Constant 0.712 0.489 0.513
Lecturer’s Ability 0.341 0.235 3.121 0.004 Significant
Lecturer’s and Student’s Activity
in the classroom 0.257 0.215 2.116 0.045 Significant
Student’s Response 0.413 0.436 5.244 0.000 Significant
Fvalue = 25.454
Sig F = 0.000
The following is the multiple linear regression output.
ŷ = 0.235 X1 + 0.215X2 +0.436X3+e
An analysis of the simultaneous effects of the independent variables (lecturer ability, lecturer and student activity in the classroom, and student response) on the dependent variables (student learning outcome) using the F-test was done for hypothesis testing. The F-value was set at 25.454 based on the test (significant when F=0.000). Sig F 5% (0.000-0.05) indicates that the dependent variable (student learning outcome) was significantly influenced by the independent factors (lecturer skill, lecturer and student activities in the classroom, and student response). It means that if all three factors lecturer ability, student activity in the classroom, and student response increase simultaneously, the student's learning outcome will be improved.
Conversely, if all three factors lecturer ability, student activity in the classroom, and student response decrease, the learning outcome will be worsened. The premise that the skill of the lecturer, the activity of the lecturer and students in the classroom, and the response of the student have a substantial impact on the learning outcomes of the student was therefore accepted. A t- test was also performed to assess the relative impact of each independent variable on the dependent variable. The test's outcome was explained in further detail as follows:
1. The ability of the lecturer had a t-value of 3.121, which was significant at t=0.004. The lecturer's skill (X1) significantly influenced the learning outcomes of the students (Y) because it was less than 5% (0.0040 < 0.05).
Because the standardized coefficient of regression was positive (0.235), the independent variable positively influenced the dependent variable. In other words, the outcomes of the students' learning will improve as the lecturer's skills develop.
2. The t-value for the interaction between the lecturer and the students in the class was 2.116 (significant when t = 0.045).
The activity of the lecturer and students in the classroom (X2) significantly affects the learning outcomes (Y) of students because it was less than 5 percent (0.0450<0.05). The independent variable has a positive influence on the dependent one, as indicated by the positive standardized coefficient of regression (0.215). In other words, better learning outcomes for students will result from increased activity among the lecturer and students in the classroom.
3. The student's response had a t-value of 5.244, which is significant when t=0.000. The student's response (X3) significantly affects the student's learning results (Y) because it was less than 5 percent (0.00000<0.05). The independent variable has a positive influence on the dependent one, as indicated by the positive standardized coefficient of regression (0.436). In other words, better learning outcomes will result from more student responses.
One independent variable that significantly influences the dependent variable is indicated by the standardized coefficient of regression (also known as beta). The independent variable with the highest standardized coefficient of regression is the most important. According to Table 1, the student answer (X3) has the largest beta coefficient (0.436), indicating that it has the most significant impact on the learning outcomes of the students.
Coefficient of Determination
The coefficient of determination demonstrated the extent to which the independent variables—lecturer skill (X1), student and lecturer participation in class (X2), and student reaction (X3)—could account for the learning outcome of the student. The variables' coefficients of determination were displayed in table 2.
Table 2. Coefficient of Determination Model Summary
Model R R Square Adjusted R Square Std. an error of the Estimate
1 .854a .732 .715 1.178
The adjusted R square was 0.715, or 71.50%, according to Table 2. It means that 71.5 % of the learning outcome can be explained by the lecturer's skill, the students' participation and responses in class, and the students' responses, while the remaining 28.5 % can be explained by other factors outside the scope of the study.
DISCUSSION
Effect of Lecturer Skills on Students' Learning Results
The results showed that the lecturer's ability (X1) had a coefficient of regression of 0.235, indicating that the lecturer's ability had a favorable impact on a student's learning outcome. In other words, better lecturer skills will result in better learning results for the students. It enhances pupils' comprehension of tsunamis in geography. Because the lesson plans addressed both
the teacher's and the students' activities in the classroom in detail, the respondents had a favorable opinion of the lecturer's skills.
The study's findings support Long et al., (2014); Sudargini & Purwanto, (2020) findings that lecturer competency influences students' learning outcomes. According to Piaget as described in Slavin (1994), learners form schemata through interaction with their surroundings; therefore, teachers should create a welcoming learning environment for their students.
Students' Learning Outcome
The activity of the lecturer and the students in the classroom (X2) had a regression coefficient of 0.215, indicating that this activity has a beneficial impact on the learning outcomes of the students. Because the lecturers have created learning materials that adhere to the paradigm, increasing student and lecturer participation in the classroom would enhance students' learning results. According to Arends (1997), a lecturer's analytic abilities have a favorable impact on the pupils. Additionally, Nur (1999) made the case that one of the elements influencing the quality of learning is the learning environment. To improve students' comprehension of tsunami and their repercussions, this study created a tsunami-based digital map as a learning tool for disaster mitigation themes. The results confirm Slavin (1994) theory that students actively seek out principles while lecturers take on the role of facilitators. The qualities of the lesson are what are needed to fulfill this condition; for students to fully grasp a subject, additional explanation and experimentation are required. It exemplifies lecturer-based learning and highlights the importance of lecturers in the classroom (Arends, 1997). According to several studies, the results of student learning have been significantly influenced by the appropriate learning media (Puspitarini & Hanif, 2019; Rozal et al., 2021; Schneider et al., 2018).
It will benefit students' learning outcomes when teachers apply digital maps to educate geography students about the Tsunami subtopic.
Response of the student to the learning outcome
The coefficient of regression of the student's response (X3) was 0.436 which means the student's response has a positive influence on the learning outcome. As a result, better learning outcomes will result from more student answers.
According to the survey results, students' opinions on the use of a tsunami-based digital map in disaster prevention lectures have a big impact on their learning outcomes. Students have a positive attitude toward the learning components, the topic of discussion offers fresh information and is elaborated, the students are interested in the developed learning medium, and after hearing about the digital map, they are motivated to participate in the subsequent lesson, according to a descriptive analysis of the students' attitudes toward the learning components. According to Atwater (1996), teaching materials should motivate students to participate in the learning process. The findings of this study are consistent with other studies showing that various teaching materials can enhance student motivation and interest in learning as well as learning outcomes (Hsiao & Su, 2021; Hsu et al., 2019).
Effects of the lecturer's skill, the students' participation in class, and their responses on the students' learning outcomes The results showed that the dependent variable, student learning outcome, is significantly influenced by the independent factors, lecturer skill, lecturer and student activities in the classroom, and student reaction. It means that if all three factors lecturer ability, student activity in the classroom, and student response increase simultaneously, the student's learning outcome will be improved. Conversely, if all three factors lecturer ability, student activity in the classroom, and student response decrease, the learning outcome will be worsened. The premise that the skill of the lecturer, the activity of the lecturer and students in the classroom, and the reaction of the student have a substantial impact on the learning outcomes of the student was thus accepted. 71.50%, or 0.715, was the Adjusted R Square. It indicates that 71.5 percent% of the learning outcome can be explained by the lecturer's skill, the students' participation and responses in class, and the students' responses, while the remaining 28.5 percent may be explained by other factors beyond the scope of the study. According to the percentage, the three independent variables lecturer skill, lecturer and student participation in class, and student response have a substantial impact on the learning result. The findings of this study support those of Choe et al., (2019); Munir et al., (2018), who found that three factors lecturer abilities, in-class student activities, and student responses need to function effectively together to promote increasing student learning outcomes.
The respondent's response is classified as a fair improvement based on the descriptive analysis. This progress is consistent with Wulandari's study, which found that students' knowledge of disaster mitigation is improving, albeit slowly. The improvement shows that pupils have received adequate instruction in disaster mitigation and are ready to respond to disasters whenever they occur. The goal of disaster mitigation is to reduce the number of fatalities during an emergency. The results of this study correlate with those of (Liu et al., 2018). Study, which showed that using digital media, particularly digital maps, can enhance students' skills to lower disaster risk. The creation of knowledge leads to students' understanding of catastrophe mitigation, which becomes relevant and contextual via experience. Students actively contribute to the creation of their new knowledge, enabling them to use it in specific situations. Students actively generate information and use their knowledge through constructivism. Students' knowledge of disaster mitigation is the outcome of the development of knowledge, and
through experience, their knowledge becomes relevant and contextual. Because they actively participate in creating their new knowledge, students can use it in specific situations. Students actively develop knowledge and put it to use through constructivism.
After reviewing the students' comments, it can be concluded that they have a highly favorable opinion of the digital map centered on natural disasters. Positive feedback was received on the second factor. When the digital map was utilized for discussion, it indicated that the students had a favorable attitude toward both the digital map’s content and the learning environment. The third digital map component, typography, picture, and picture location also received favorable feedback from the students. Pictures from places near the students' homes, such Puget Beach, were included in the lesson. The results of this study are in line with those of other studies that demonstrate how different teaching materials may improve both learning outcomes and student motivation and interest in learning (Hsiao & Su, 2021; Hsu et al., 2019). According to the discussion above, the important point is that using digital maps enhances students' abilities to mitigate risk. This is also impacted by the lecturer's skills, the students' participation in class, and their attitude toward learning.
CONCLUSSION
Based on the results and discussion, several conclusions can be drawn, including that (i) lecturers' abilities have a big impact on students' learning outcomes in geography (specifically, students' ability to use tsunami-based digital maps as learning resources for disaster mitigation classes); (ii) lecturer and student activities in class; (iii) Student responses have a significant impact on geography students' ability to use tsunami-based digital maps as learning resources for disaster mitigation classes; (iv) Student responses have a significant impact on geography students' ability to use tsunami-based digital maps as learning resources for disaster mitigation classes. Additionally, the three independent variables lecturers' expertise, students' participation in class activities, and students' responses have a substantial impact on geography students' capacity to utilize tsunami-based digital maps as a learning tool in disaster mitigation classes (student learning outcomes). Future studies should include a larger population than simply one class of Samudra University geography students considering these findings. Before adopting tsunami-based digital maps as a teaching tool for classes on disaster mitigation, geography lecturers must also be adequately prepared.
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