Measuring student motivation: how the formulation of a question affects a response
Analysis of the immediate impact of the applied methods on the work results. Survey, question formulation and wording. Motivation-measuring scales. The main results of the analysis that were carried out in order to test the main hypothesis of the study.
| Рубрика | Социология и обществознание |
| Вид | дипломная работа |
| Язык | английский |
| Дата добавления | 16.08.2020 |
| Размер файла | 2,0 M |
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56
Химико-биологический
50
Each scale consists of five items. Table No. 4 shows the average values for each of the 5 scales used in the questionnaire with the standard deviation values.
Table 4
|
Scales |
Means (SD) |
|
|
performance |
3,2 (1,7) |
|
|
learning |
3,4 (1,6) |
|
|
avoidance |
3,2 (1,4) |
|
|
confidence |
3,4 (1,7) |
|
|
support |
3,4 (1,7) |
All of them deviate between 3 and 4 (which stands for the answers “Скорее не согласен” and “Скорее согласен”). Such results are not surprising - it would seem to be a mistake if the answers were strongly skewed towards one of the response scale values.
Visualization
The graphs below show how students responded to the questionnaire scales (Figure No. 1-5). Each graph is divided into two parts - "no" and "yes", according to the positive and negative survey.
Figure 1-5
The given graphs show the distribution of answers to each scale in positive and negative formulations. However, it is difficult to compare the statistical difference between the results of questionnaires as a whole based on this visualization. The only striking accent is the absence of answers 4 and 5 (i.e. "agree" and "fully agree") in all scales for negative formulations of questions.
4. Results
This chapter will present the main results of the analysis that were carried out in order to test the main hypothesis of the study. The hypothesis lies in the assumption that there is a difference between the students' answers to the opposite wording of the question. Consequently, the wording of the question affects the answer.
hypothesis motivation scale method
4.1 T.test results
Student's t-test gives an opportunity to compare the means for two groups while comparing the given distribution with the normal one. T-test was conducted for each of five scales, comparing means for two groups of data - positive and negative formulations. Since a negative questionnaire is reversed relative to a positive questionnaire, it is necessary to "turn over" the results of the questionnaire first in order to correctly perform the t-test. To do this, each value of the answer for a negative survey has been replaced with the opposite value: "Completely disagree" (1) to "Completely agree" (6), "Disagree" (2) to "Agree" (5), and so on. This procedure is valid because accepting the positive wording means the same as rejecting the negative wording.
Table 5
|
Welch Two Sample t-test |
|
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|
data: all_answers$Performance by all_answers$`Positive (yes/no)` |
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|
t = 3.4651, df = 257.91, p-value = 0.0006206 |
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|
alternative hypothesis: true difference in means is not equal to 0 |
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|
95 percent confidence interval: |
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|
0.225 0.819 |
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|
sample estimates: |
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|
mean in group no mean in group yes |
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|
4.472 3.950 |
Table 6
|
Welch Two Sample t-test |
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data: all_answers$Learning by all_answers$`Positive (yes/no)` |
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|
t = 1.7671, df = 282.71, p-value = 0.0783 |
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|
alternative hypothesis: true difference in means is not equal to 0 |
||||
|
95 percent confidence interval: |
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|
-0.020 0.379 |
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|
sample estimates: |
||||
|
mean in group no mean in group yes |
||||
|
4.401 4.221 |
Table 7
|
Welch Two Sample t-test |
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data: all_answers$Avoidance by all_answers$`Positive (yes/no)` |
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|
t = 8.3791, df = 276.52, p-value = 2.726e-15 |
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|
alternative hypothesis: true difference in means is not equal to 0 |
||||
|
95 percent confidence interval: |
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|
0.398 0.642 |
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|
sample estimates: |
||||
|
mean in group no mean in group yes |
||||
|
4.140 3.620 |
Table 8
|
Welch Two Sample t-test |
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data: all_answers$Confidence by all_answers$`Positive (yes/no)` |
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t = 1.3086, df = 302.73, p-value = 0.1917 |
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|
alternative hypothesis: true difference in means is not equal to 0 |
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|
95 percent confidence interval: |
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-0.0625 0.3111 |
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|
sample estimates: |
||||
|
mean in group no mean in group yes |
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|
4.246 4.121 |
Table 9
|
Welch Two Sample t-test |
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data: all_answers$Support by all_answers$`Positive (yes/no)` |
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|
t = 2.2761, df = 302.52, p-value = 0.02354 |
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|
alternative hypothesis: true difference in means is not equal to 0 |
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|
95 percent confidence interval: |
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|
0.028 0.390 |
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|
sample estimates: |
||||
|
mean in group no mean in group yes |
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|
4.311 4.101 |
This test allows the comparison of how one continuous variable differs in two data groups. In this case, the dataset is divided into two groups - according to positive and negative formulations. Each table (4-8) reflects the test results for each questionnaire scale and variable that corresponds to the wording of the question. The p-value results for the conducted tests are less than 0.05, which shows a significant difference in the positive and negative responses. However, for two scales the p-value is greater than 0.05, which means that the difference in the responses of the questionnaires was not statistically significant - such conclusion is relevant for the scales Learning (Motivation towards learning) and Confidence (Confidence in teacher).
4.2 Exploratory Factor Analysis
The difference between answers has already been found. However, that is not the last step of the data analysis. It is necessary to see, whether the theoretical model of factors is working, and the described scales are valid. For this purpose, two stages should be implied: exploratory factor analysis and confirmatory factor analysis. The exploratory factor analysis helps to reveal the connections in the real obtained data - it represents, how do the data “sum” in factors independently to the theoretical model of the study. The given analysis would give an opportunity to see, whether the “real” scales differ from ones defined by the theory. In the case of this diploma, there was believed to be five scales (three on motivation and two on teacher-student relationships), each of which have five items in them.
Table 10
EFA for negative questionnaire
|
Factor loadings for EFA on negative questionnaire |
|||||
|
MR1 |
MR2 |
MR4 |
MR3 |
||
|
MTP1 |
0.703 |
||||
|
MTP2 |
0.561 |
0.359 |
|||
|
MTP3 |
0.558 |
0.384 |
|||
|
MTP4 |
0.716 |
||||
|
MTP5 |
0.980 |
||||
|
MTL1 |
0.338 |
-0.378 |
0.376 |
||
|
MTL2 |
0.700 |
0.403 |
|||
|
MTL3 |
0.513 |
||||
|
MTL4 |
0.621 |
||||
|
MTL5 |
0.645 |
||||
|
MTA1 |
0.570 |
-0.364 |
|||
|
MTA2 |
0.932 |
||||
|
MTA3 |
0.568 |
||||
|
MTA4 |
0.738 |
||||
|
MTA5 |
0.311 |
0.556 |
|||
|
CIT1 |
0.766 |
0.337 |
|||
|
CIT2 |
0.747 |
||||
|
CIT3 |
0.822 |
||||
|
CIT4 |
0.923 |
||||
|
CIT5 |
0.829 |
||||
|
TS1 |
0.837 |
||||
|
TS2 |
0.803 |
||||
|
TS3 |
0.860 |
||||
|
TS4 |
0.910 |
||||
|
TS5 |
0.870 |
Picture 2
Picture 2 represents the model generated by the EFA algorithm and Table 10 contains item loadings for each factor. As a recommended number of scales applied the analysis output shows 4 factors. Thus, the input parameters for the construction of the diagram were filtered by negative questionnaire dataset and the number of scales equal to 4.
As can be noted, the algorithm has defined the scales related to teacher-student relationships as one factor (MR1). In general, this conclusion makes sense, because the two specified scales (Confidence in teacher and Support) are indeed very similar in their meaning. Thus, for further analysis I will apply this conclusion and treat them as a single factor with 10 items. Regarding the remaining three scales, the conclusion is not so unambiguous. The algorithm combines items into three scales as follows: MR2 factor consists of Performance and Learning scale questions, MR3 factor combines Avoidance scale questions. As for MR4 scale - it requires more detailed consideration, as it contains only two parameters - MTA-1 and MTP-5. MTP-5 item is the coded question “Улучшить мой общий средний балл в дипломе не является для меня самой важной задачей”, MTA-1 item is the following statement: “Я совсем не переживаю, когда пишу экзамен/ контрольную/ самостоятельную работу”. For further work with CFA, the results of EFA will be taken as an input theoretical model - thus three outcome factors will be used, to which questions are allocated. They are MR1 (or “Relationships”, which consists of primary Confidence and Support scales), MR2 (or “Perform & Learn”, primary Performance and Learning scales) and MR3 plus MR4 (or “Avoidance” scale) for negative questionnaire.
Table 11
EFA for positive questionnaire
|
Factor loadings for EFA on positive questionnaire |
|||||
|
MR2 |
MR1 |
MR3 |
MR4 |
||
|
MTA-1 |
0.558 |
0.366 |
|||
|
MTA-2 |
-0.808 |
||||
|
MTA-3 |
-0.875 |
||||
|
MTA-4 |
-0.969 |
||||
|
MTA-5 |
0.456 |
0.411 |
0.586 |
||
|
MTL-1 |
0.598 |
0.300 |
|||
|
MTL-2 |
0.605 |
0.588 |
|||
|
MTL-3 |
0.494 |
0.660 |
|||
|
MTL-4 |
0.654 |
0.566 |
|||
|
MTL-5 |
0.454 |
0.668 |
|||
|
MTP-1 |
0.896 |
||||
|
MTP-2 |
0.899 |
||||
|
MTP-3 |
0.950 |
||||
|
MTP-4 |
0.859 |
||||
|
MTP-5 |
0.968 |
||||
|
TS-1 |
0.641 |
-0.344 |
0.306 |
||
|
TS-2 |
0.896 |
||||
|
TS-3 |
0.882 |
-0.305 |
|||
|
TS-4 |
0.865 |
||||
|
TS-5 |
0.686 |
-0.321 |
0.436 |
||
|
CIT-1 |
0.629 |
0.434 |
|||
|
CIT-2 |
0.871 |
||||
|
CIT-3 |
0.918 |
||||
|
CIT-4 |
0.892 |
||||
|
CIT-5 |
0.755 |
Picture 3
Picture 3 reflects the result of the exploratory factor analysis for the positive questionnaire and Table 11 depicts item loadings. In this case, unlike the dataset with negative answers, the algorithm immediately recommends using three factors for the model. The first factor (MR2) again brings together the teacher-student relationship scales, as with the negative questionnaire. The second and third factors are not so clear - these are the mixes of Performance, Learning and Avoidance scales. The factor MR1 when addressing item loadings may be consider as “Performance” scale. The last factor, MR3, combines items from Avoidance and Learning scales (it can be called “Learn & Avoid”) with three items of Avoidance scale having negative item loadings.
Even when investigating the results of these steps, it may be concluded that results for positive and negative questionnaires differ - the data is formed in two pictures, that are not similar to each other. One more conclusion is that scales are worth to be rebuilt - probably, the theoretical model was irrelevant for the given population or the scales are not clearly seen because of the small amount of data received. Nevertheless, additional stage of the data analysis that has to be done is CFA analysis with new input information - models from EFA.
4.3 Confirmatory factor analysis
The CFA method enables one to see whether the real data obtained in the study is consistent with the theoretical model that was originally developed. As the input parameters, the algorithm accepts the described model and database. The model may include components, relationships between them, which are formed into a factor. With the help of CFA it will be possible to check whether the items really add up to these five factors and how each parameter is related to the final factor. For example, some items can be combined into one factor but have opposite item loadings. This would mean that the question and the scale have opposite directions. The theoretical model presented in the previous paragraphs was used as input parameters for the analysis, as well as a filtered answer dataset. Separate models were built for the Motivation Scales and the Confidence and Support Scales. At the first step, the scales defined by EFA were applied in CFA modelling. However, the results were not valid (the detailed output for all models are attached in the Appendix).
4.3.1 Positive questionnaire
Performance scale
Table 12 represents the model fit measures for the first model. The model was based on positive questionnaire and scale “Performance” (MR1 from the previous paragraph with EFA).
Table 12
|
Number of observations 148 |
||||
|
Estimator ML |
||||
|
Minimum Function Test Statistic 38.670 |
||||
|
Degrees of freedom 5 |
||||
|
P-value (Chi-square) 0.000 |
||||
|
Model test baseline model: |
||||
|
Minimum Function Test Statistic 1004.871 |
||||
|
Degrees of freedom 10 |
||||
|
P-value 0.000 |
||||
|
User model versus baseline model: |
||||
|
Comparative Fit Index (CFI) 0.966 |
||||
|
Tucker-Lewis Index (TLI) 0.932 |
||||
|
Loglikelihood and Information Criteria: |
||||
|
Loglikelihood user model (H0) -921.722 |
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|
Loglikelihood unrestricted model (H1) -902.388 |
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|
Number of free parameters 10 |
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|
Akaike (AIC) 1863.445 |
||||
|
Bayesian (BIC) 1893.417 |
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|
Sample-size adjusted Bayesian (BIC) 1861.771 |
||||
|
Root Mean Square Error of Approximation: |
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|
RMSEA 0.213 |
||||
|
90 Percent Confidence Interval 0.154 0.278 |
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|
P-value RMSEA <= 0.05 0.000 |
The size of P-value (Chi-square) is less than 0.05, which represents the good estimate for model. However, this index is very sensitive to the sample size, so the conclusion should not be established on its value only. The next parameter - Comparative Fit Index (CFI) equals 0.966. The higher is this estimate, the more accurate a model is. The value is high enough, which depicts good model fit. The value of Root Mean Square Error of Approximation is less than 0.2 with p-value less than 0.05 - the less this estimate is, the better the model is. The RMSEA value is too low, comparing to the “threshold” values.
Table 13
Learning scale
|
Number of observations 148 |
||
|
Estimator ML |
||
|
Minimum Function Test Statistic 22.230 |
||
|
Degrees of freedom 5 |
||
|
P-value (Chi-square) 0.000 |
||
|
Model test baseline model: |
||
|
Minimum Function Test Statistic 565.810 |
||
|
Degrees of freedom 10 |
||
|
P-value 0.000 |
||
|
User model versus baseline model: |
||
|
Comparative Fit Index (CFI) 0.969 |
||
|
Tucker-Lewis Index (TLI) 0.938 |
||
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Loglikelihood and Information Criteria: |
||
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Loglikelihood user model (H0) -865.138 |
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Number of free parameters 10 |
||
|
Akaike (AIC) 1750.276 |
||
|
Bayesian (BIC) 1780.248 |
||
|
Root Mean Square Error of Approximation: |
||
|
RMSEA 0.153 |
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90 Percent Confidence Interval 0.092 0.220 |
||
|
P-value RMSEA <= 0.05 0.005 |
The model 2 has the following input parameters: filtered dataset with positive questionnaire results and learning scale. CFI value for model 2 equals 0.969 with 0.938 TLI (Table 13). The values are above threshold, so the good model fit is represented. As for the RMSEA, it equals 0.15, which is not close to zero enough.
Table 14
Avoidance scale
|
Number of observations 148 |
||
|
Estimator ML |
||
|
Minimum Function Test Statistic 104.527 |
||
|
Degrees of freedom 5 |
||
|
P-value (Chi-square) 0.000 |
||
|
Model test baseline model: |
||
|
Minimum Function Test Statistic 556.447 |
||
|
Degrees of freedom 10 |
||
|
P-value 0.000 |
||
|
User model versus baseline model: |
||
|
Comparative Fit Index (CFI) 0.818 |
||
|
Tucker-Lewis Index (TLI) 0.636 |
||
|
Loglikelihood and Information Criteria: |
||
|
Loglikelihood user model (H0) -876.426 |
||
|
Number of free parameters 10 |
||
|
Akaike (AIC) 1772.852 |
||
|
Bayesian (BIC) 1802.824 |
||
|
Root Mean Square Error of Approximation: |
||
|
RMSEA 0.367 |
||
|
90 Percent Confidence Interval 0.307 0.430 |
||
|
P-value RMSEA <= 0.05 0.000 |
Model 3 represents results of CFA conducted on avoidance scale items and positive questions (Table 14). It may be seen that CFI value is too low, as well as RMSEA is very high. It means, that this particular model is inaccurate and unsatisfying.
Table 15
Confidence in teacher and Teacher's support scales
|
Number of observations 148 |
||
|
Estimator ML |
||
|
Minimum Function Test Statistic 635.817 |
||
|
Degrees of freedom 35 |
||
|
P-value (Chi-square) 0.000 |
||
|
Model test baseline model: |
||
|
Minimum Function Test Statistic 2016.161 |
||
|
Degrees of freedom 45 |
||
|
P-value 0.000 |
||
|
User model versus baseline model: |
||
|
Comparative Fit Index (CFI) 0.695 |
||
|
Tucker-Lewis Index (TLI) 0.608 |
||
|
Loglikelihood and Information Criteria: |
||
|
Loglikelihood user model (H0) -1267.033 |
||
|
Number of free parameters 20 |
||
|
Akaike (AIC) 2574.066 |
||
|
Bayesian (BIC) 2634.010 |
||
|
Root Mean Square Error of Approximation: |
||
|
RMSEA 0.341 |
||
|
90 Percent Confidence Interval 0.318 0.364 |
||
|
P-value RMSEA <= 0.05 0.000 |
As was defined by the exploratory factor analysis, the items from theoretical scales “Confidence in teacher” and “Teacher's support” were combined into one final factor. Indeed, these questions are very close in their meaning. Nevertheless, when conducting the confirmatory factor analysis on these items and positive questionnaire, the model fit results are not high. CFI and TLI are way below the threshold; RMSEA measure is too big. Probably, this happens because of the mix of intercorrelated items and reverse-meaning items. Unfortunately, the separate CFA with confidence and support scales also do not show acceptable model fit measures (Table 16), according to RMSEA values, but CFI value for them are a little bit higher.
Table 16
|
|
p-value (Chi-squared) |
Comparative Fit Index (CFI) |
Tucker-Lewis Index (TLI) |
Root Mean Square Error of Approximation (P-value RMSEA <= 0.05) |
|
|
Model "Support" |
0.000 |
0.915 |
0.83 |
0.282 (0.000) |
|
|
Model "Confidence" |
0.000 |
0.916 |
0.832 |
0.265 (0.000) |
|
|
Model "Support and confidence" |
0.000 |
0.695 |
0.608 |
0.341 (0.000) |
4.3.2 Negative questionnaire
The logic behind CFA analysis for the negative questionnaire is identical to the positive one. The theoretical model is based on the EFA results. Three factors are going to be checked: MR1 (or “relationships”, which consists of primary confidence and support scales), MR2 (or “perform & learn”, primary performance and learning scales) and MR3 plus MR4 (or “avoidance” scale) for negative questionnaire.
Table 17
Support and confidence scales
|
Number of observations 157 |
||
|
Estimator ML |
||
|
Minimum Function Test Statistic 338.320 |
||
|
Degrees of freedom 35 |
||
|
P-value (Chi-square) 0.000 |
||
|
Model test baseline model: |
||
|
Minimum Function Test Statistic 1777.220 |
||
|
Degrees of freedom 45 |
||
|
P-value 0.000 |
||
|
User model versus baseline model: |
||
|
Comparative Fit Index (CFI) 0.825 |
||
|
Tucker-Lewis Index (TLI) 0.775 |
||
|
Loglikelihood and Information Criteria: |
||
|
Loglikelihood user model (H0) -1434.805 |
||
|
Number of free parameters 20 |
||
|
Akaike (AIC) 2909.611 |
||
|
Bayesian (BIC) 2970.735 |
||
|
Root Mean Square Error of Approximation: |
||
|
RMSEA 0.235 |
||
|
90 Percent Confidence Interval 0.212 0.258 |
||
|
P-value RMSEA <= 0.05 0.000 |
Table 17 represents model fit measures for the CFA with factor, which combines items from support and confidence scales. As can be seen, the CFI and TLI measures are too low, and RMSEA value is too high. The model is unsatisfying.
However, when conducting the separate CFA for these two scales (which were combined into one during EFA), the results are a little bit higher (presented in table 18). The CFI for both models is higher than 0.9.
Table 18
|
Support scale |
Confidence scale |
|
|
Number of observations 157 |
Number of observations 157 |
|
|
Estimator ML |
Estimator ML |
|
|
Minimum Function Test Statistic 57.989 |
Minimum Function Test Statistic 21.419 |
|
|
Degrees of freedom 5 |
Degrees of freedom 5 |
|
|
P-value (Chi-square) 0.000 |
P-value (Chi-square) 0.001 |
|
|
Model test baseline model: |
Model test baseline model: |
|
|
Minimum Function Test Statistic 656.109 |
Minimum Function Test Statistic 576.542 |
|
|
Degrees of freedom 10 |
Degrees of freedom 10 |
|
|
P-value 0.000 |
P-value 0.000 |
|
|
User model versus baseline model: |
User model versus baseline model: |
|
|
Comparative Fit Index (CFI) 0.918 |
Comparative Fit Index (CFI) 0.971 |
|
|
Tucker-Lewis Index (TLI) 0.836 |
Tucker-Lewis Index (TLI) 0.942 |
|
|
Loglikelihood and Information Criteria: |
Loglikelihood and Information Criteria: |
|
|
Loglikelihood user model (H0) -748.756 |
Loglikelihood user model (H0) -828.878 |
|
|
Loglikelihood unrestricted model (H1) -719.761 |
Loglikelihood unrestricted model (H1) -818.168 |
|
|
Number of free parameters 10 |
Number of free parameters 10 |
|
|
Akaike (AIC) 1517.511 |
Akaike (AIC) 1677.756 |
|
|
Bayesian (BIC) 1548.074 |
Bayesian (BIC) 1708.318 |
|
|
Sample-size adjusted Bayesian (BIC) 1516.420 |
Sample-size adjusted Bayesian (BIC) 1676.664 |
|
|
Root Mean Square Error of Approximation: |
Root Mean Square Error of Approximation: |
|
|
RMSEA 0.260 |
RMSEA 0.145 |
|
|
90 Percent Confidence Interval 0.202 0.322 |
90 Percent Confidence Interval 0.085 0.210 |
|
|
P-value RMSEA <= 0.05 0.000 |
P-value RMSEA <= 0.05 0.007 |
Performance, Learning and Avoidance scales
The CFA results for the last three scales (Performance, Learning and Avoidance) are the highest one among all built models for positive and negative questionnaire. The short summary of key model fit measures is presented in Table 19. All outputs of R studio code are attached in the appendix, where the full description of CFA measures is given.
Table 19
|
|
p-value (Chi-squared) |
Comparative Fit Index (CFI) |
Tucker-Lewis Index (TLI) |
Root Mean Square Error of Approximation (P-value RMSEA <= 0.05) |
|
|
Model "Performance" |
0.013 |
0.982 |
0.964 |
0.110 (0.059) |
|
|
Model "Learning" |
0.428 |
1.000 |
1.003 |
0.000 ( 0.555) |
|
|
Model "Avoidance" |
0.751 |
1.000 |
1.019 |
0.000 ( 0.870) |
As can be seen from the table, CFI measures are almost equal or equal to 1. The RMSEA value also is quite close to zero. These models work well on positive questionnaire, which leads to the conclusion, that positive and negative variants' results differ significantly, and students answered not in a similar manner for the two survey forms.
5. Conclusions
Turning to the general conclusions of the data analysis, the t-test revealed the significant difference in answers for positive and negative formulations (after reversing the negative-questionnaire answers). CFA results turned out to be not high enough for some models. First of all, it may be explained by the items in the scales that were found during the literature review. Due to the fact, that they were firstly filtered due to the possibility of their translation into Russian, then they were adapted (and changed a little bit) to fit the Russian context the items may have become not quite accurate. Secondly, of course, low model fit measures may be caused by poor quality of collected data. New method of data collection (online survey) is not always the good way to collect valid data - although the link was sent to pupils through the official channel, the attrition rate was high and not all student of 10th and 11th grades were taking part in the research. Probably, if the study could be replicated in the offline field, the results would differ.
Explaining these results in the terms of hypothesis and research question of the diploma, the difference between answers on two variants is significant - the wording of the question effected the responses. Answers for the positive worded items were “lower” compared with the negative ones - pupils disagreed with the negative statements more than agreed with the positive statements. Probably, if the sample size was bigger, the effect would be more prominent.
As the practical conclusion, it can be summarized, that the use of mixed scales with polar worded items is a tricky way to balance the acquisition and response style. Indeed, the items could seem to have the exact same meaning, but respondents do understand them differently - the negative items, perhaps, are considered to be “stricter”, so participants had disagreed with them more than agreed to the positive, “optimistic” formulations.
Finally, the research had received its aim - the hypothesis is accepted, and the practical finding was formulated. The further purposes would be to replicate the study offline and/or using another context - it would help to see whether the conclusion is valid only in the described environment or for all possible circumstances.
6. Limitations of the study
A key limitation of the work was the need to change the data collection method. The initial method of data collection was supposed to be a face-to-face survey of the pupils of the gymnasium. I was going to visit the "class" hours of the 11th and 10th grades, during which a link to the survey would be sent. Unfortunately, due to insurmountable circumstances, it was forced to resort to an online survey. Of course, this circumstance has affected the sample quality. Not all students responded to the questionnaire that was sent them by their teachers. Despite that, it can be said that the students complied with the condition of passing the survey by variants, judging by the equal amount of data for each questionnaire.
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8. Appendix
Annex No. 1 (List of questions by scales)
|
Источник |
Название шкалы |
Original question |
Перевод (позитивная формулировка) |
Перевод (негативная формулировка) |
|
|
Midgley, C., Kaplan, A., Middleton, M., Maehr, M. L., Urdan, T., Anderman, L. H., Anderman, E., & Roeser, R. (2013). Patterns of Adaptive Learning Scales [Data set]. American Psychological Association. https://doi.org/10.1037/t19870-000 |
motivation towards performance |
(1) For me, getting good grades is the main goal |
(1) Для меня, получение хороших оценок - это главная цель обучения в школе. |
(1) Для меня, получение хороших оценок - это не самая главная цель обучения в школе. |
|
|
Midgley, C., Kaplan, A., Middleton, M., Maehr, M. L., Urdan, T., Anderman, L. H., Anderman, E., & Roeser, R. (2013). Patterns of Adaptive Learning Scales [Data set]. American Psychological Association. https://doi.org/10.1037/t19870-000 |
(2) Students who get good grades are pointed out as an example to others |
(2) Для меня, те ученики, которые получают высокие оценки, являются примером |
(2) Для меня, те ученики, которые получают высокие оценки, не являются примером |
||
|
Midgley, C., Kaplan, A., Middleton, M., Maehr, M. L., Urdan, T., Anderman, L. H., Anderman, E., & Roeser, R. (2013). Patterns of Adaptive Learning Scales [Data set]. American Psychological Association. https://doi.org/10.1037/t19870-000 |
(3) t's important to me that other students in my class think I am good at my class |
(3) Для меня важно, чтобы другие ученики в моем классе видели, что я хорошо учусь по всем предметам |
(3) Для меня не важно, чтобы другие ученики в моем классе видели, что я хорошо учусь по всем предметам |
||
|
Duncan, T., Pintrich, P., Smith, D., & Mckeachie, W. (2015). Motivated Strategies for Learning Questionnaire (MSLQ) Manual. https://doi.org/10.13140/RG.2.1.2547.6968 |
(4) I want to do well in this class because it is important to show my ability to my family, friends, employer, or others |
(4) Одна из причин хорошо учиться - это показать свои способности семье, друзьям, будущему работодателю и тд |
(4) Показать свои способности семье, друзьям, будущему работодателю и тд не является для меня причиной хорошо учиться |
||
|
Duncan, T., Pintrich, P., Smith, D., & Mckeachie, W. (2015). Motivated Strategies for Learning Questionnaire (MSLQ) Manual. https://doi.org/10.13140/RG.2.1.2547.6968 |
(5) The most important thing for me right now is improving my overall grade point average, so my main concern in this class is getting a good grade |
(5) Самая важная задача для меня - это улучшение моего общего среднего балла в дипломе |
(5) Улучшить мой общий средний балл в дипломе не является для меня самой важной задачей |
||
|
Midgley, C., Kaplan, A., Middleton, M., Maehr, M. L., Urdan, T., Anderman, L. H., Anderman, E., & Roeser, R. (2013). Patterns of Adaptive Learning Scales [Data set]. American Psychological Association. https://doi.org/10.1037/t19870-000 |
motivation towards learning |
(1) It's important to understand the work, not just memorize it |
(1) Для меня более важная задача - понять материал, а не просто хорошо запомнить его. |
(1) Для меня, понять материал является менее важной задачей, чем хорошо его запомнить |
|
|
Duncan, T., Pintrich, P., Smith, D., & Mckeachie, W. (2015). Motivated Strategies for Learning Questionnaire (MSLQ) Manual. https://doi.org/10.13140/RG.2.1.2547.6968 |
(2) I ask the instructor to clarify concepts I don't understand well |
(2) Я прошу преподавателя объяснить материал, который я плохо понял(а) |
(2) Я избегаю просить преподавателя объяснить материал, который я плохо понял(а) |
||
|
Duncan, T., Pintrich, P., Smith, D., & Mckeachie, W. (2015). Motivated Strategies for Learning Questionnaire (MSLQ) Manual. https://doi.org/10.13140/RG.2.1.2547.6968 |
(3) It is important for me to learn the course materi... |
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