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English Learning Motivation of College Students Based on probability Distribution

Publié en ligne: 15 Jul 2022
Volume & Edition: AHEAD OF PRINT
Pages: -
Reçu: 09 Mar 2022
Accepté: 13 May 2022
Détails du magazine
License
Format
Magazine
eISSN
2444-8656
Première parution
01 Jan 2016
Périodicité
2 fois par an
Langues
Anglais
Probability distribution
Definition 1

Simply speaking, probability distribution function is mainly used to describe the basic law of random variable value distribution. For all real numbers x, the probability of the event [x Assuming F(x) = P (X < x), it can be obtained: F()=0,F()=1 F\left({- \infty} \right) = 0,F\left(\infty \right) = 1

Theorem 1

This formula is called the distribution function of the random variable X. Thus, the distribution function F (x) completely affects the probability of events, in other words, the distribution function F (x) comprehensively presents the statistical characteristics of random variables.

For example, for the probability distribution of discrete random variable, it can be assumed that x1, x2,..., xn is the value of variable X, and p1, p2,..., pn represents the probability of the above values. Then the calculation formula of the probability distribution of discrete random variable X is as follows: P(X=xi)=pi,i=1,2,,n P\left({X = {x_i}} \right) = {p_i},i = 1,2, \ldots,n

Proposition 2

At the same time, probability Pi meets the following conditions: i=1np1=1 \sum\limits_{i = 1}^n {{p_1} = 1}

Thus, the probability distribution function of discrete random variable X is: F(x)=xi<npi F\left(x \right) = \sum\limits_{{x_i} < n} {{p_i}}

Lemma 3

In the analysis of the probability distribution of continuous random variables, it is assumed that the value interval of variable X is [a, B], the distribution function is a monotone increment function, differentiable under the condition of ===, and the actual derivative F (X) is continuous within the interval, then the probability that variable X falls within the interval [x, (x + Δx)] is: P(xXx+Δx)=F(x+Δx)F(x) P\left({x \le X \le x + \Delta x} \right) = F\left({x + \Delta x} \right) - F\left(x \right)

In order to describe the law of probability distribution, the density function of probability distribution should be used to define it, as shown below: f(x)=F(x)=limΔx0F(x+Δx)F(x)Δx f\left(x \right) = F^{'}\left(x \right) = \mathop {\lim}\limits_{\Delta x \to 0} {{F\left({x + \Delta x} \right) - F\left(x \right)} \over {\Delta x}}

Corollary 4

At this point, the probability distribution function can be transformed into the probability integral form, as shown below: F(x)=xf(x)dx F\left(x \right) = \int_{- \infty}^x {f\left(x \right)dx}

Given the probability distribution density function of a continuous random variable X, the specific probability of variable X in a certain interval (x1, X2) can be calculated, and the specific formula is as follows: P(x1Xx2)=F2(x2)F(x1)=x1x2f(x)dx P\left({{x_1} \le X \le {x_2}} \right) = {F_2}\left({{x_2}} \right) - F\left({{x_1}} \right) = \int_{{x_1}}^{{x_2}} {f\left(x \right)dx}

Then the corresponding distribution density function is: f(x)0,f(x)dx=1 f\left(x \right) \ge 0,\int_{- \infty}^\infty {f\left(x \right)dx = 1}

Conjecture 5. It can be seen that the probability distribution function is the main performance of the characteristics of random variables, which affects the distribution law of the value of random variables. On the basis of the clear probability distribution function, the probability of random variables falling at a certain place can be accurately calculated.

Using the bucket ranking algorithm in probability distribution, this paper analyzes the survey data of college students' English learning motivation, mainly discusses how to evenly distribute the ranking records among all buckets, which is also the key factor to improve the ranking technology. From the point of view of the current statistical data application, it mainly obeys a certain probability distribution in a certain interval. For this kind of data sorting, the hash function can be constructed according to the density function of probability distribution, and then the N records to be arranged are evenly distributed to N buckets according to the size of key code value, so as to ensure that the running time of bucket sorting can reach O (n) under all conditions.[1]

Computer internal sorting algorithm is mainly divided into two kinds, one kind is refers to the comparison of sorting algorithm, research scholars at home and abroad the analysis is improved, not only increase the application rate, also have more data distribution, originally is the best features of Ω time consumption, and improved characteristics can be O (n). The other is the use of numbers and address computing as the core of the sorting algorithm. The critical code finite type is 1,... In the case of m, the calculation time complexity of the algorithm is O (m+n).[2.3]

Reordering the records in all buckets using other sorting techniques. The purpose is to allocate fewer records to each bucket with a less expensive bucket processing technique, and to complete the record sorting of all buckets according to a faster end sorting. Therefore, how to evenly distribute the waiting sort records to all buckets is the core content of the promotion bucket sorting technology.

Since the investigation of college students' English learning motivation involves a lot of data information, the sorting algorithm following a certain probability distribution is used to construct a hash function, so as to evenly distribute the multiple records waiting for sorting into buckets, so as to improve the efficiency and quality of data research.

basis
Theorem 2

In all intervals, assuming that there are n records of key code A [I]. Key obeying a certain probability distribution, the density function of probability distribution can be used to evenly distribute all records to the bucket.

It is proved that if the density function of probability distribution on interval [c, d] in key code A [I].key is φ (x), then the interval [C, d] should be divided into n intercells by using special points, so as to ensure that the area of curvilinear trapezoid formed by X-axis and curve is consistent on intercell [ck, ck+1]. At this point, the area calculation formula of all intervals is as follows: 1ncdϕ(x)dx {1 \over n}\int_c^d {\phi \left(x \right)dx}

According to the analysis of probability theory, it is found that the corresponding probability of the same area between the segments is also similar. If each interval corresponds to a bucket and all data in array A [n] has the same probability of falling into the bucket, data records can be evenly distributed into the bucket.

Note that since c=c0, c1..., cn=d is not necessarily the equipartition point, so the actual position cannot be determined. From the point of view of practical application, it is not necessary to specify the location of the point, as long as the correct calculation can master the key code area of the waiting sorting record.

Assume that S (a[I].key) represents the serial number of the bucket where A [I] resides. The actual calculation formula is as follows: s(a[i].key)=[ca[i].keyϕ(x)dx1ncdϕ(x)dx] s\left({a\left[i \right].{key}} \right) = \left[{{{\int_c^{a\left[i \right].{key}} {\phi \left(x \right)dx}} \over {{1 \over n}\int_c^d {\phi \left(x \right)dx}}}} \right]

In the above formula, [] stands for integer, which is consistent with the following studies. This formula stands for the hash function.

Theorem 3

If s(a[i].key) < s(a[j].key), then [i].key < a[j].key.

Prove

Assuming s(a[i].key) < s(a[j].key), we can get: [ca[i].keyϕ(x)dx1ncdϕ(x)dx]<[ca[j].keyϕ(x)dx1ncdϕ(x)dx] \left[{{{\int_c^{a\left[i \right].{key}} {\phi \left(x \right)dx}} \over {{1 \over n}\int_c^d {\phi \left(x \right)dx}}}} \right] < \left[{{{\int_c^{a\left[j \right].{key}} {\phi \left(x \right)dx}} \over {{1 \over n}\int_c^d {\phi \left(x \right)dx}}}} \right]

Because the condition ϕ(x) > 0 is met in the interval [c, d], it can be obtained: 1ncdϕ(x)dx>0 {1 \over n}\int_c^d {\phi \left(x \right)dx} > 0

It can also be made clear: ca[i].keyϕ(x)dx<ca[j].keyϕ(x)dx \int_c^{a\left[i \right].{key}} {\phi \left(x \right)dx} < \int_c^{a\left[j \right].{key}} {\phi \left(x \right)dx}

In other words, a[i].key < a[j].key

A key with a small serial number must be small. If records are sorted according to the size of the serial number, the actual key has orderliness.

Calculation Method

Example 6. To study college students' English learning motivation based on probability distribution, we should study the obedience conditions of key codes from two aspects: one is uniform distribution, the other is normal distribution.

From the perspective of uniform distribution, assuming that the compliance condition of waiting sort key a[n]. Key on interval [C, d] is uniform distribution, the actual density function is shown as follows: ϕ(x)=1dc \phi \left(x \right) = {1 \over {d - c}}

Combining the above formula to calculate the number of bucket where a[n]. Key resides, the following formula can be obtained: s(a[i].key)=[ca[i].keyϕ(x)dx1ncdϕ(x)dx] s\left({a\left[i \right].{key}} \right) = \left[{{{\int_c^{a\left[i \right].{key}} {\phi \left(x \right)dx}} \over {{1 \over n}\int_c^d {\phi \left(x \right)dx}}}} \right]

From the perspective of normal distribution, the actual density function formula is as follows: ϕ(x)=12πxe(xm)22π2 \phi \left(x \right) = {1 \over {\sqrt {2\pi x}}}{e^{{{- {{\left({x - m} \right)}^2}} \over {2{\pi ^2}}}}}

In the above formula, m represents the mean and Z represents the mean square deviation.

At the same time, although the original function of the integral of the normal distribution function φ (x) exists, it cannot be expressed by the elementary function, so a function should be constructed in the interval [C, d] to approximate the function φ (x). It should be noted that the curve of sinx function on interval [π2,3π2] \left[{- {\pi \over 2}, - {{3\pi} \over 2}} \right] is very similar to the curve of function φ (x) on interval [c, d]. Therefore, only valid coordinate and function conversions are required. The function can better approximate the normal distribution function φ (x). Because both φ (x) and sinx functions belong to symmetric functions, [c, d] represents the actual distribution space of key code A [I].key. Generally speaking, the average is mdc2 m \ne {{d - c} \over 2} , so the interval [c, d] should be appropriately expanded to [c′, d′], the specific formula is: c=mmax(mc,dm)d=m+max(mc,dm) \eqalign{& c^{'} = m - \max \left({m - c,d - m} \right) \cr & d^{'} = m + \max \left({m - c,d - m} \right) \cr}

Thus, it can be obtained: m=dc2 m = {{d^{'} - c^{'}} \over 2}

And meet the following conditions: [c,d][c,d] \left[{c,d} \right] \subseteq \left[{c^{'},d^{'}} \right]

Assuming pm = ϕ(m), the formula for calculating the change of the function is: φ(x)=m2(sin(2x(xc)dcπ2)+1) \varphi \left(x \right) = {m \over 2}\left({\sin \left({{{2x\left({x - c^{'}} \right)} \over {d^{'} - c^{'}}} - {\pi \over 2}} \right) + 1} \right)

After transformation, the function still has the mean and mean square deviation of the original normal distribution density function. The calculation result of the mean is as follows: m=1ni=1na[i].key m = {1 \over n}\sum\limits_{i = 1}^n {a\left[i \right].{key}}

The calculation results of mean square error Z are as follows: z2=1ni=1n(a[i].keym)2=1ni=1n(a[i].key22a[i].key.m+m2)=1ni=1n(a[i].key22mi=1na[i].key+i=1nm2)=(i=1na[i].key2nm2+nm2)=(i=1na[i].key2nm2) \eqalign{& {z^2} = {1 \over n}\sum\limits_{i = 1}^n {{{\left({a\left[i \right].{key} - m} \right)}^2} =} \cr & {1 \over n}\sum\limits_{i = 1}^n {\left({a\left[i \right].{key}^2 - 2a\left[i \right].{key}.m + {m^2}} \right) =} \cr & {1 \over n}\sum\limits_{i = 1}^n {\left({a\left[i \right].{key}^2 - 2m\sum\limits_{i = 1}^n a \left[i \right].{key} + \sum\limits_{i = 1}^n {{m^2}}} \right) =} \cr & \left({\sum\limits_{i = 1}^n {a\left[i \right].{key} - 2n{m^2} + n{m^2}}} \right) = \cr & \left({\sum\limits_{i = 1}^n {a\left[i \right].{key}^2 - n{m^2}}} \right) \cr}

After the coordinate transformation, the interval can be effectively extended according to the symmetry of function φ (x), but there is no distributed key code inside. When calculating the serial number, the integral interval still stays in the original [C, D] interval. At this point, the serial number calculation formula is as follows[4.5]: s(a[i].key)=[ca[i].keyφ(x)dx1ncdφ(x)dx]=[nca[i].keypm2(sin(2π(xc)dcπ2)+1)dxcdpm2(sin(2π(xc)dcπ2)+1)dx] s\left({a\left[i \right].{key}} \right) = \left[{{{\int_c^{a\left[i \right].{key}} {\phi \left(x \right)dx}} \over {{1 \over n}\int_c^d {\phi \left(x \right)dx}}}} \right] = \left[{{{n\int_c^{a\left[i \right].{key}} {{{pm} \over 2}\left({\sin \left({{{2\pi \left({x - c^{'}} \right)} \over {d^{'} - c^{'}}} - {\pi \over 2}} \right) + 1} \right)dx}} \over {\int_c^d {{{pm} \over 2}\left({\sin \left({{{2\pi \left({x - c^{'}} \right)} \over {d^{'} - c^{'}}} - {\pi \over 2}} \right) + 1} \right)dx}}}} \right]

Assumptions: k=ncd(sin(2π(xc)dcπ2)+1)dx k = {n \over {\int_c^d {\left({\sin \left({{{2\pi \left({x - c^{'}} \right)} \over {d^{'} - c^{'}}} - {\pi \over 2}} \right) + 1} \right)dx}}}

Then we can get: s(a[i].key)=[K(dc2πcos(2π(xc)dcπ2)+x)ca[j].key]=[K(dc2πcos(2π(a[i].keyc1)dcπ2)+a[i].key+dc2πcos(2π(cc)dcπ2)c)]1.3 \eqalign{& s\left({a\left[i \right].{key}} \right) = \left[{K\left({- {{d^{'} - c^{'}} \over {2\pi}}\cos \left({{{2\pi \left({x - c^{'}} \right)} \over {d^{'} - c^{'}}} - {\pi \over 2}} \right) + x} \right)\int_c^{a\left[j \right].{key}} {}} \right] = \cr & {\left[{K\left({- {{d^{'} - c^{'}} \over {2\pi}}\cos \left({{{2\pi \left({a\left[i \right].{key} - c1} \right)} \over {d^{'} - c^{'}}} - {\pi \over 2}} \right) + a\left[i \right].{key} + {{d^{'} - c^{'}} \over {2\pi}} - \cos \left({{{2\pi \left({c - c^{'}} \right)} \over {d^{'} - c^{'}}} - {\pi \over 2}} \right) - c} \right)} \right]^{1.3}} \cr}

Algorithm Analysis

During data sort, it is clear that the array to be read into is a[n], and the corresponding type is: struct ss [floatkey;structss×next;]a[n]; \left[{float\,key;struct\,ss \times next;} \right]a\left[n \right];

Meanwhile, the data type of pointer array q[n] is: int×q[n+1] {\mathop{\rm int}} \times q\left[{n + 1} \right]

The description of the sorting algorithm is as follows:

FIG. 1

Algorithm flow chart

Research design
Problem Analysis

In view of the current situation of college students' English learning, this paper conducts a questionnaire survey on students' learning motivation based on probability distribution. Practical problems involve the following points: First, to understand the self-state of college students' English learning motivation; Secondly, the self-systematic characteristics of English learning motivation of students of different ages, majors, genders and levels should be clarified. Finally, the impact of the differences on English language teaching should be deeply explored.

Research Methods

In this paper, the probability distribution method is used to collect relevant data and information, and quantitative and qualitative methods are combined for in-depth discussion. The actual research tool is the self-questionnaire of motivation proposed by Taguchi, Magid, Papi et al., and the questionnaire of college students' English learning motivation proposed by Liu Fengge et al. In the design of the questionnaire, two aspects are mainly concerned: on the one hand, the background investigation of students; on the other hand, combined with the second language motivational self system theory, the four aspects of learning motivation intensity, ideal self, should self and learning experience of students are systematically investigated. After completing the initial questionnaire, the Likert rating scale was used to set the option score, and the one-to-one semi-structured interview was proposed in combination with qualitative analysis.[6.7]

Collecting Data

In this study, a certain university was taken as the data source, and questionnaires were sent to college students through QQ group or wechat group. After 400 questionnaires were collected, invalid questionnaires that did not meet the requirements due to disciplines, age and other factors were removed, and 369 valid samples were finally obtained. Statistical software S P S S 17.0 was used to analyze the probability distribution and validate the validity of the data, and descriptive statistical analysis was conducted on the participants' second language learning motivation self-system, which involved correlation, one-way variance, robustness and other test methods.

Result analysis
Motivation Overview

Combined with the results of the self-questionnaire on college students' second language motivation as shown in Table 1 below, it is found that the mean values of emotion and behavior in motivation intensity are both over the median of 3.5, which proves that college students' English learning motivation is positive, but the actual level is not high. The overall average value of ideal self and learning experience is more than 3.5, which proves that these two factors are dominant influencing factors, which often appear in students' English learning and are the main contents of learning motivation. The overall mean value of the should self is the median, so it is a non-dominant factor, but it contains components, whether defensive motivation or family influence factors are dominant factors. According to these four description result, the study found the ideal self has the highest average value, so the second language ego ideal for college students of English learning effect is the largest, the second is the learning experience, this and other scientific research scholar's research results both at home and abroad, thus proves that motivation system yourself in English learning motivation research in our country has a certain application advantages.

Descriptive statistical results of the motivational self-questionnaire

The number of The mean The standard deviation
The intensity of motivation The emotion motive 369 3.85 1.55
Motivation behavior 369 3.89 1.35
Ideal self International vision 369 5.26 1.35
Facilitative tool motivation 369 5.63 1.26
Integrative instrumental motivation 369 5.21 1.58
Should be self Family influence 369 5.12 1.38
Defensive motivation 369 3.55 1.53
Contextual motivation 369 2.83 1.58
Learning experience Learn confidence 369 5.39 1.28
Learning anxiety 369 3.36 1.58
Age

This paper uses one-way anOVA to compare and discuss the differences in English learning motivation among college students of different grades. The actual results are shown in Table 2 below:

Results of self-variance test of motivation of college students of all grade

A freshman A sophomore Junior year Senior year The F value Df2 Sig value
The emotion motive 4.03 3.42 3.84 3.58 3.132 0.025
Motivation behavior 4.04 3.08 4.07 3.40 174.213 0.011
International vision 4.28 4.14 4.24 4.70 1.875 0.133
Facilitative tool motivation 4.42 4.55 4.48 4.90 0.949 0.417
Integrative instrumental motivation 4.34 4.07 4.09 4.44 1.504 0.188
Family influence 4.19 4.08 4.20 3.89 0.330 0.803
Defensive motivation 3.35 3.54 3.59 3.41 0.947 0.418
Contextual motivation 2.54 2.82 3.27 2.90 1.927 0.124
Learn confidence 4.48 4.29 4.43 4.35 0.757 0.413
Learning anxiety 3.08 3.49 3.27 3.83 3.332 0.020

Note :(df2) is the result of robustness test when homogeneity of variance is not satisfied

According to the analysis results in the table above, there are significant differences in learning anxiety, motivated behavior and emotional motivation, while there are no significant differences in other parts. From the average value, the order of emotional motivation is freshman, junior, senior and sophomore; The ranking results of behavioral motivation were junior, freshman, senior and sophomore. The ranking results of learning anxiety were senior year, sophomore year, junior year and freshman year. In practice, some researchers found that the higher the grade of college students, the stronger the motivation of English learning, which is quite different from the research results in this paper. The reason for this phenomenon is that there is a discrepancy between the actual questionnaire and the subjects of the study. According to the analysis of relevant numerical changes, it is found that the students with the strongest emotional learning motivation are the students with the strongest behavioral motivation. Through one-to-one interviews with students, it is found that students have higher interest in learning and higher actual expectations. However, due to the lack of professional and systematic guidance and management, students do not have a strong sense of independent control, so they cannot achieve good learning results. Sophomores' behavioral motivation and emotional motivation are all at the lowest level, because their professional courses are getting more and more difficult, their knowledge is too boring, and their interest in English learning is getting lower and lower. At this stage, students are in a downturn. Most of the junior college students are facing the postgraduate entrance examination or examination, the actual learning motivation is stronger; However, as senior students have basically completed their undergraduate learning tasks, their actual motivation to learn English begins to decline slowly.[9.10]

gender

The T test of independent samples was used to judge the differences between boys and girls in the motivational self system, and the specific results are shown in Table 2 below:

Sample test results of male and female students in the motivational self system

The mean T value
female male
The intensity of motivation The emotion motive 4.14 3.34 0.004
Motivation behavior 3.83 3.49 0.337
Ideal self International vision 4.39 4.01 0.009
Facilitative tool motivation 3.83 4.24 0.000
Integrative instrumental motivation 4.44 3.74 0.000
Should be self Family influence 4.17 4.02 0.291
Defensive motivation 3.44 3.72 0.094
Contextual motivation 2.47 3.12 0.004
Learning experience Learn confidence 4.44 4.07 0.001
Learning anxiety 3.34 3.37 0.948

According to the research results in the table above, the emotional motivation value of both male and female students is 0.005, which proves that there is a significant difference in their motivation during English learning. On average, girls are higher than boys. However, the constitutions of motivation behavior are all over 0.05, which proves that girls' interest in learning is higher than boys', but there is no significant difference in learning behavior. It can be seen that gender differences in self are not obvious, but the actual mean value proves that male students have stronger defensive motivation than female students and are highly susceptible to the influence of the surrounding environment.

professional

According to the results of self-variance test of motivation shown in Table 3 below, there are significant differences in students' motivation under different academic backgrounds. Among them, science students have higher learning emotional motivation and motivated behavior, and art students have the lowest learning motivation.

Results of motivational self-examination for each discipline backgroun

Academic background and motivation Families In science and engineering The arts The F value Df2 Sig value
The intensity of motivation The emotion motive 3.73 3.92 3.34 3.384 0.035
Motivation behavior 3.82 3.91 3.38 193.014 0.048
Ideal self International vision 4.17 4.42 4.09 1.839 0.160
Facilitative tool motivation 4.54 4.76 4.52 1.435 0.299
Integrative instrumental motivation 4.27 4.10 4.34 0.778 0.460
Should be self Family influence 4.17 4.16 3.88 1.043 0.354
Defensive motivation 3.37 3.68 3.63 1.749 0.175
Contextual motivation 2.55 2.98 3.17 192.006 0.009
Learning experience Learn confidence 4.40 4.44 4.23 0.583 0.559
Learning anxiety 3.23 3.34 3.79 3.136 0.045

Note :(df2) is the result of robustness test when homogeneity of variance is not satisfied

Learning Level

According to the self-examination results of motivation of students at different levels as shown in Table 4 below, primary means below LEVEL 4, intermediate means between level 4 and level 6, and senior means above level 6.

Results of self-examination of motivation of students of different levels

English proficiency and motivation primary The intermediate senior The F value Df2 Sig value
The intensity of motivation The emotion motive 3.59 4.29 4.71 10.924 0.000
Motivation behavior 3.66 4.16 4.67 8.847 0.000
Ideal self International vision 4.17 4.56 4.83 3.958 0.000
Facilitative tool motivation 4.53 4.87 5.38 90.636 0.000
Integrative instrumental motivation 4.14 4.47 4.67 2.162 0.117
Should be self Family influence 4.08 4.14 4.54 1.253 0.287
Defensive motivation 3.55 3.68 3.13 1.083 0.340
Contextual motivation 2.74 3.33 2.92 3.130 0.045
Learning experience Learn confidence 4.34 4.43 5.00 3.080 0.047
Learning anxiety 3.39 3.49 2.79 2.004 0.136

Note :(df2) is the result of robustness test when homogeneity of variance is not satisfied

Combined with the numerical analysis in the table above, it is found that students at different English learning levels have significant differences in experience, ideal self and motivation intensity. For example, in motivation intensity, the higher the English learning level of college students, the higher the behavioral motivation and emotional motivation; In terms of ideal second language self, the higher the English learning level of college students, both the stimulative tool and the international perspective motivation are constantly enhanced; During the application of SECOND language self, although there are obvious differences due to the influence of surrounding environmental factors, the actual average value is not a significant result, which proves that there is no significant difference in second language self. This research result is consistent with the content proposed by current scientific research scholars. In addition, students at different learning levels also have different self-confidence. The stronger the actual ability, the higher the self-learning consciousness, which is consistent with the conventional phenomenon.[11.12]

Conclusion

To sum up, combined with probability distribution for the current college students' English learning motivation system investigation, not only can clearly recognize the students learning situation, can also according to the study demand effective intervention countermeasures, and actively to strengthen students' self-confidence, prompt them to eliminate bad feelings at the same time, to raise their level of English learning. Therefore, on the basis of integrating the relevant topics and research results of domestic and foreign scholars, colleges and universities should put forward effective educational guidance schemes aiming at the current situation of college English teaching and learning, so as to optimize students' English experience on the basis of strengthening their learning motivation.

FIG. 1

Algorithm flow chart
Algorithm flow chart

Results of self-examination of motivation of students of different levels

English proficiency and motivation primary The intermediate senior The F value Df2 Sig value
The intensity of motivation The emotion motive 3.59 4.29 4.71 10.924 0.000
Motivation behavior 3.66 4.16 4.67 8.847 0.000
Ideal self International vision 4.17 4.56 4.83 3.958 0.000
Facilitative tool motivation 4.53 4.87 5.38 90.636 0.000
Integrative instrumental motivation 4.14 4.47 4.67 2.162 0.117
Should be self Family influence 4.08 4.14 4.54 1.253 0.287
Defensive motivation 3.55 3.68 3.13 1.083 0.340
Contextual motivation 2.74 3.33 2.92 3.130 0.045
Learning experience Learn confidence 4.34 4.43 5.00 3.080 0.047
Learning anxiety 3.39 3.49 2.79 2.004 0.136

Results of motivational self-examination for each discipline backgroun

Academic background and motivation Families In science and engineering The arts The F value Df2 Sig value
The intensity of motivation The emotion motive 3.73 3.92 3.34 3.384 0.035
Motivation behavior 3.82 3.91 3.38 193.014 0.048
Ideal self International vision 4.17 4.42 4.09 1.839 0.160
Facilitative tool motivation 4.54 4.76 4.52 1.435 0.299
Integrative instrumental motivation 4.27 4.10 4.34 0.778 0.460
Should be self Family influence 4.17 4.16 3.88 1.043 0.354
Defensive motivation 3.37 3.68 3.63 1.749 0.175
Contextual motivation 2.55 2.98 3.17 192.006 0.009
Learning experience Learn confidence 4.40 4.44 4.23 0.583 0.559
Learning anxiety 3.23 3.34 3.79 3.136 0.045

Results of self-variance test of motivation of college students of all grade

A freshman A sophomore Junior year Senior year The F value Df2 Sig value
The emotion motive 4.03 3.42 3.84 3.58 3.132 0.025
Motivation behavior 4.04 3.08 4.07 3.40 174.213 0.011
International vision 4.28 4.14 4.24 4.70 1.875 0.133
Facilitative tool motivation 4.42 4.55 4.48 4.90 0.949 0.417
Integrative instrumental motivation 4.34 4.07 4.09 4.44 1.504 0.188
Family influence 4.19 4.08 4.20 3.89 0.330 0.803
Defensive motivation 3.35 3.54 3.59 3.41 0.947 0.418
Contextual motivation 2.54 2.82 3.27 2.90 1.927 0.124
Learn confidence 4.48 4.29 4.43 4.35 0.757 0.413
Learning anxiety 3.08 3.49 3.27 3.83 3.332 0.020

Sample test results of male and female students in the motivational self system

The mean T value
female male
The intensity of motivation The emotion motive 4.14 3.34 0.004
Motivation behavior 3.83 3.49 0.337
Ideal self International vision 4.39 4.01 0.009
Facilitative tool motivation 3.83 4.24 0.000
Integrative instrumental motivation 4.44 3.74 0.000
Should be self Family influence 4.17 4.02 0.291
Defensive motivation 3.44 3.72 0.094
Contextual motivation 2.47 3.12 0.004
Learning experience Learn confidence 4.44 4.07 0.001
Learning anxiety 3.34 3.37 0.948

Descriptive statistical results of the motivational self-questionnaire

The number of The mean The standard deviation
The intensity of motivation The emotion motive 369 3.85 1.55
Motivation behavior 369 3.89 1.35
Ideal self International vision 369 5.26 1.35
Facilitative tool motivation 369 5.63 1.26
Integrative instrumental motivation 369 5.21 1.58
Should be self Family influence 369 5.12 1.38
Defensive motivation 369 3.55 1.53
Contextual motivation 369 2.83 1.58
Learning experience Learn confidence 369 5.39 1.28
Learning anxiety 369 3.36 1.58

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