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Gifted students have different ways of learning. They are characterized by a fitful level of attention and intuitive reasoning. In order to distinguish gifted students from normal students, we conducted an experiment with 17 pupils, willing participants in this study. We collected different types of data (gender, age, performance, initial average in math and EEG mental states) in a web platform called NetMath intending for the learning of mathematics. We selected ten tasks divided into three difficulty levels (easy, medium and hard). Participants were invited to respond to top-level exercises on the four basic operations in decimals. Our first results confirmed that the student’s performance has no relation with age. A younger 9-year-old student achieved a higher score than the group with an average of 68.18%. This student can be considered as a gifted one. The gifted students can be also characterized by a mean value of attention (around 60%). They also can be defined by slightly weaker values of their mental states of attention and workload in comparison with the weak pupils.

Nowadays, the performance of learners in primary schools differs from one individual to another. We find more students who perform below the average than others whose performance exceeds the group average. The latter is often much more advanced than other students and is bored in class because the presented information is already obvious to them and too easy. We were talking about gifted, talented or high creativity students. According to Zettel (1979), general intelligence usually manifests in intelligent quotient (IQ). Gifted students have an IQ of 130 or above. These students are “endowed by nature with high intellectual capacity and have a native capacity for high potential intellectual attainment and scholastic achievement [

In literature, several studies have been conducted to identify and detect gifted students. Most studies focus on the measurement of intelligence quotient (IQ) with psychometric tests such as the Wechsler Intelligence Scale for Children (WISC) [

The paper is organized as follows. In Section 2 we talk about related works in assessing and identifying gifted students. Section 3 describes our experimental environment designed to evaluate student’s performance in mathematics, called NetMath. In Section 4, we describe the experiment conducted in a primary school. Finally, Section 5 shows our obtained results in term of gifted students achievement and EEG mental states variation.

Historically, there are many definitions and conceptualizations of gifted and talented students [

Although there is not a way to measure giftedness and intelligence, researchers agree that intellectual measurement or intelligence quotient measurement (IQ) can be considered as an intelligence measurement. IQ is a numerical value that reflects the overall intelligence of the person [

Both hemispheres of the brain (right/left) and their successful interaction play a crucial role in the complex process of mathematics [

NetMath Platform^{1} is a web application to support learning mathematics for primary and secondary students (from 3rd primary grade to 4th secondary grade). It contains a set of tasks and exercises in different topics of Math such probability, statistic, decimal numbers, fractions, etc. In our case, we focus on evaluating the topic of decimal numbers for 4th and 5th grade primary students. We are mainly interested in performing the four basic operations on decimal numbers (addition, subtraction, multiplication and division).

In order to evaluate students’ performance and EEG traits in NetMath platform, we choose a total of 10 tasks from NetMath platform designed to 6th grade students. These tasks are divided into three levels of difficulty: easy, medium and hard as described below.

In these tasks, the student is asked to do one or two operations on decimals (adding or subtracting two numbers). An example of this task is presented in

In these tasks, the operations are presented in problems that are more complicated and where the student has to do more than one operation at same time. Therefore, we think that he has to be more careful in order to succeed in these tasks.

Two difficult problems are presented to the students. The problems required a greater mental effort and the students have to think carefully in order to resolve these problems.

of the one-dollar money dimensions. It requires two multiplications and one subtraction.

In order to detect bright and gifted students, we conducted an experiment where we asked elementary school students (4th and 5th grades) to resolve the selected tasks from NetMath environment described above. The proposed tasks were designed for higher-level students (6th grade). A consent form preceded the experiment where we obtained the agreement of each parent to let his/her child to participate in our study. The experiment was held at École Samuel de Champlain (Brossard, Canada) after the classes. 17 students (10 F, 7 M) voluntarily participated in this study with a compensation of 20$ each. Students are aged between 9 and 11 year (M = 10.05; SD = 0.42). We invite two children at the same time to do the experiment as we have two laptops and two EEG headsets.

We start the experiment by filling a short questionnaire about demographic data for each student (age, sex and math average obtained during the first step in school). Furthermore, we invite the student in login to the NetMath website and in completing the proposed tasks in ascending order (from easy to difficult). During the fulfilment of the tasks, we collect data from Neeuro Senzeband non-invasive EEG headset. This headset allows us to obtain EEG raw data from 4 channels and three mental states measures (Attention, Workload and Relaxation). This headset is heavy, easy to install and more suitable for use and experimentation with children. It collects EEG data from four sensors (two right and two left frontal lobes). Neeuro provides only an SDK for mobile phones. In order to save the EEG data in the student’s computer, we started by creating a mobile application that is connected to the EEG headset through Bluetooth. This application collects in real time the EEG data, then, it sends them to the computer via Wi-Fi and the computer saves them to a CSV file.

In this part, we present the results obtained in NetMath platform for the 10 selected exercises. This part is divided in three subparts. The first part compares the performances obtained in these tasks and the initial averages in mathematics. This comparison lets us to obtain an indication of the strongest students (bright). The second part asks the question of the influence of age on performance. Finally, the third part studies the distribution of mental states according to the performance.

In order to detect the strongest students, we calculated for each student the obtained average (from 0% to 100%) in all the tasks extracted from NetMath platform. The average performance in this environment is 59.64%. However, the obtained group average in math in the first step class is 78% which is a little bit higher, due to the difficulty of the given tasks (tasks are designed for high level students). Thus, we can distinguish two groups: Group 1 with an average higher than the obtained group average in the first step or in NetMath platform and Group 2 with a lower average.

From

Performance | First Step | NetMath | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

N | Moy | SD | Min | Max | N | Moy | SD | Min | Max | |

Group 1 | 11 | 88.63 | 6.19 | 81 | 97 | 10 | 76.71 | 11.34 | 68.18 | 90.9 |

Group 2 | 6 | 68.66 | 8.01 | 57 | 73 | 7 | 35.25 | 20.41 | 10 | 57.14 |

Total | 17 | 81.72 | 11.57 | 57 | 97 | 17 | 59.64 | 25.9 | 10 | 90.9 |

presents this distribution. We can deduce that almost all students succeeded in resolving easy tasks (53.75 out of 64: 83.89%), more than half students accomplished medium tasks, and a very few number of students (only 2) success to solve hard tasks. So, we suspect that these students are very good at school. There is a large probability that they are gifted students. This finding will be studied and proved in our next work.

In this part, we studied the influence of age on performance. We remember that our sample of students is composed of 3 ages (9, 10 and 11 years old).

From

This part discusses the variation of three mental states extracted from EEG senzeband according to student’s performance. As students could be classified into two group: Group 1 with the highest performance (>59.64%) and Group 2 with the lowest performance (<59.64%), we present below two curves which indicates the EEG mental states distribution among each group.

From

Age | Performance | ||||
---|---|---|---|---|---|

N | Moy | SD | Min | Max | |

9 | 1 | 68.18* | 0 | 68.18 | 68.18 |

10 | 14 | 63.72* | 25.2 | 10 | 90.9 |

11 | 2 | 26.78 | 2.52 | 25 | 28.57 |

* indicates that the average is higher than the student’s average in our experiment (59.64%).

Group 1 (bright students) have a stable attention value of 60% (see

In this paper, we described a study aiming to distinguish high ability math students from average students. This study is based on the measure of the performance, age, and EEG mental states of attention, workload and relaxation. Our results show that bright and talented students succeed to answer top-level math exercises with a performance of 90%. We can also characterize bright students with an average value of attention (60%) and average students by fluctuated values of attention (very high or very low). Future work will focus on comparing the results extracted from Raven Progressive Matrices and EEG raw data. We will study tendencies in EEG variation for the right and left hemispheres and power bands (Alpha, Beta, Theta and Delta).

This research was supported by the FRQNT (Fonds de Recherche du Québec en Nature et Technologies) and Beam me up company. We thank also Mrs Christine Nadeau for welcoming us in her school (École Samuel de Champlain, Brossard, Canada) and the professors of 4th and 5th grades.

The authors declare no conflicts of interest regarding the publication of this paper.

Ghali, R., Abdessalem, H.B., Frasson, C. and Nkambou, R. (2018) Identifying Brain Characteristics of Bright Students. Journal of Intelligent Learning Systems and Applications, 10, 93-103. https://doi.org/10.4236/jilsa.2018.103006