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opencc-by-4.0May 2024View details →

Medical Students use Online Study Materials more than School-Provided Resources when preparing for USMLE Step 1

In this excel file contains the raw data collected from a survery sent out to students attend ULSOM and UNRSOM. The raw data was processed and analyzed in the sheet titled "graphs".

Fig. 3 in Navigating through ocean literacy gaps: an analysis of elementary school textbooks in Croatian education Abstract

Fig. 3: Presence of ocean literacy principles (OLP) and concepts (OLC) in higher grades of the elementary school science textbooks in Croatia (grades 5-8). Results are presented as average occurrence and standard deviation of books analysed for each grade.

Fig. 2 in Navigating through ocean literacy gaps: an analysis of elementary school textbooks in Croatian education Abstract

Fig. 2: Presence of ocean literacy principles (OLP) and concepts (OLC) in lower grades of the elementary school science textbooks in Croatia (grades 1-4). Results are presented as average occurrence and standard deviation of books analysed for each grade.

Fig. 1 in Navigating through ocean literacy gaps: an analysis of elementary school textbooks in Croatian education Abstract

Fig. 1: Mean contribution of pages with ocean-related topics in the text (A) and illustration (B) in the Croatian elementary school textbooks. Error bars represent the standard deviation among different publishers or textbook lines.

Fig. 4 in Navigating through ocean literacy gaps: an analysis of elementary school textbooks in Croatian education Abstract

Fig. 4: Best-case scenario of presence of ocean sciences topics according to OL principles and concepts in the elementary school textbooks in Croatia.

opencc-by-4.0Jun 2024View details →

Assessment of Fair Trade education programs in France: high school data (2019-2022)

see the technical report (period 2019-2021) on researchgate: <a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a>

opencc-by-4.0Dec 2022View details →

Figure 2 in Prevalence of cestodes infection among school children of urban parts of Lower Dir district, Pakistan

Figure 2. Tapeworm species eggs. (A) Taenia saginata; (B) Hymenolepis nana; (C) Hymenolepis diminuta.

opencc-by-4.0Sep 2023View details →

Fig. 4 - The school adopts a in Archive reports and memories. The Brera Botanical Garden of Milan (1982-2001)

Fig. 4 - The school adopts a monument, activity carried out by the Garden and the Parini School in the school years 1996-1997 and 1997-1998. / La scuola adotta un monumento, attività coordinata dall'Orto e dalla Scuola Media Parini negli anni scolastici 1996-1997 e 1997-1998. (Courtesy Università degli Studi di Milano - MOBE, Museo Orto Botanico di Brera ed Erbario).

opencc-by-4.0Nov 2016View details →

Brain Journal-The Status of Positive Psychology Strengths within the Romanian School in the Digital Society -Figure 1. Pie chart vitalitysuccess

The Pie charts above illustrate the distribution of responses for the "Vitality" strength. It can be noticed how the lower values are much more frequent than the high ones, in terms of promoting this strength in school. Thus, the participants at this study consider that school does not attach enough importance to this strength, even though it has proven to be so mandatory in assuring the students' well-being and personal success.

opencc-by-4.0Jun 2016View details →

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 13. School girl presenting Vădastra School's Facebook page

In order to achieve the third educational level, we experimented the social media tools, i.e. the project’s Google+ educational blog, by posting information complementary to that available on the Time Maps website, and also thematic questionnaires to foster a question-answer (Q&A) learning style. This allowed us to monitor and evaluate the information retention. In this stage we encountered the challenge to stimulate children and teachers to continue using our learning system, by posting new educational content, announcements and social messages. After a period of experimentation we proceeded to a statistical evaluation of children’s answers, which is presented in the next section.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 9. Step by step drawing of the selected route solution

When the solution that is desired to be viewed is double clicked, the connections between bus stops are drawn in turn, and the route is shown as can be seen in Figure 9.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 7. Application main form which includes route, bus stops and GA parameters

For this study, the school bus routes within the Ankara Province were used as case studies. The school bus routes were recorded by using the Android application and instantaneous GPS monitoring method. The home address of each student was taken as a stopping point. At the end of each route, the distances between the beginning and end points were recorded. After transferring the obtained route data into the database, the ill-adapted points were eliminated. By means of the developed application, the existing school bus routes are dynamically optimized using GA. It was developed both as a mobile and desktop application.  Using Android- based mobile software, the information regarding GPS locations of bus stops and school buses is transferred to the server on a real-time basis. Using the desktop software, where GAs are run, these coordinates are shown on a Google map, and the most suitable route is produced and sent to the school bus via server. This method provides the opportunity to dynamically reflect certain factors such as a different initial point for the school bus, some students being absent from the school on a particular day, and eventual changes on the existing roads on the route production process.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process

The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding

Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA

The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 10. Obtained experimental results

The GA procedure was carried out using the parameter values that can be seen in Table 1, for a total of 10 school bus services’ routes identified by means of mobile-based software for a school located in the Ankara Province. The obtained experimental results are shown in Table 2 and Figure 10. The algorithm working duration also includes the formation of a distances’ matrix.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 1. Example of a VRP solution

VRP is widely described as the process where the distribution vehicles deliver goods in a depot to the clients found in geographically dispersed locations and then return to the depot on the optimum route. The VRP, first introduced by Dantzig and Ramser in 1959, aims to minimize the total distance to be covered during the routing of the vehicles in a centrally-located depot (Dantzig & Ramser, 1959). The routing process is operated by taking the vehicle capacities into consideration so as to ensure that each client located on the routing plan is visited only once. The depot, vehicle, and goods concepts mentioned in the general description of VRP can be re-adapted as school, student and bus stop, based on different sectors that the problem applies to. Figure 1 shows a scheme indicating the VRP solution applied to a distribution system, where the clientele network is dispersed over three different zones.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route

During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process

In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.

Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 2. A dynamic school bus routing case

VRP’s can also be classified into two categories as dynamic and static routing problems. In the static VRP’s, the stops/locations that the vehicle will visit are pre-specified and do not change during the distribution/collection process. In dynamic VRP’s, on the other hand, new stops can be added to the planned route during the process or certain stops can be omitted. In similar dynamic problems, some or all of the access points are not known in the beginning. These points are dynamically defined during the route design or planning stages. In the dynamic VRP, using a real- time communication network between the vehicle and decision-making system, the vehicle routes can be re-defined during the operation. This type of problems is defined as online or real-time problems by some scholars (Pillac, Gendreau, Guéret & Medaglia, 2013). Two examples of this can be certain orders getting cancelled or new orders being taken while a water distribution vehicle is on its route, or a school bus being informed on its route that certain students will be absent from school that day.  In current conditions, dynamic VRP’s are more frequently needed, and are attributed with a more specific importance. The first study dealing with dynamic VRP was carried out by Wilson and Colvin (Pillac, Gendreau, Guéret & Medaglia, 2013). The enhancements in GPS, traffic sensors, and mobile communication systems caused a further acceleration in studies carried out in this field. Within the context of this study, DSBRP will be investigated. DSBRP is graphically explained in Figure 2.