Analyzing Motivational Shifts

The hypothesis of this study is that motivation in students decreases from year 1 to year 4; whereby students shift from a mastery goal orientation to performance goal orientation to an academic alienation goal orientation.

There were 120 students that partook in this study– 45 year 1 students and 75 year 4 students. They met the strict inclusion criteria and returned the completed questionnaires. All the samples were duly filled and met the minimum thresholds for consideration in analysis. As such, the number represents 100 percent of the targeted student population in the university. The high response rate can be attributed to reachability of the participants in the campus, making it easier for students seeking university dissertation help to engage effectively.

The sample population comprised of 40.83 percent females and 59.17 percent males which closely matches the gender distribution in the university (1). Participants were from different nationalities (see table 1).

Factor analysis was used to investigate variable relationships since student profiles and motivational factors are complex concepts that cannot be measured easily. As such, a large number of variables were collapsed into a few interpretable causal factors. In factor analysis, the underpinning concept is that multiple observed variables have similar response patterns since they are related to another variable that cannot be measured – latent variable (11). In this project, it was understood that people may respond similarly to questions about goal orientation, learning strategies, preference of task and locus of control. Therefore, factor analysis was important in determining what latent variables were being suggested by the respondents. The eigenvalue has been used to measure the extent of the variance of the observed variables in each factor. As such, any factor with an eigenvalue equal to or greater than 1 suggests more variance than single observed variables (24). It was important to begin with factor analysis in order to determine what factors and variables to use in other statistical tests. In this case, factor analysis was performed on the 4 scales and their corresponding subscale variables present in the modified MAHPMS. In the test, variables only with loading values of 0.32 and beyond were considered for interpretation (25). Factor loadings explain the relationship between each variable and the underlying factors. Factor loading is the degree to which a variable is driven by a given factor. The variable with the strongest association to the underlying latent variable was determined by the closeness of its value to 1. The loading patterns for the goal orientation items when compared to similar studies undertaken by Perrot et al. (11) Further, it was found that each of the goal orientation subscales – performance, mastery and alienation − loaded on the principal factors – 4 scales− identified by this analysis. As such, the items which that related to mastery goal orientation loaded on Factor 1 (eigenvalue = 9.23, % variance = 12.26). On the other hand, the elements that had previously associated with the performance goal orientation loaded on Factor 2 (eigenvalue = 7.85, % variance = 10.52). It emerged that the elements loaded in a similar manner as Perrot et al. study (11). However, the items exhibited significantly smaller loading values than in the comparative study. In this case, they ranged from 0.326 (poor) − 0.428 (fair) for the mastery goal orientation elements and 0.344 (poor) − 0.654 (very good) for the performance goal orientation (25). Additionally, the items linking academic alienation (AA) to goal orientation loaded on Factor 1 which is similar to mastery goal elements. However, they had a negative loading as follows: (-0.375 − - 0.556). This means there is inverse proportionality in the degree of the influence between the factor and the variable. The elements attributed to learning strategies – non-cognitive learning strategies (NCLS) and metacognitive learning strategies (MLS) failed to load on independent factors. This can be attributed to the fact that some sub-clusters hang together and cannot be influenced independently. For this reason, they loaded mainly on the similar two factors as observed in the performance goal orientation (P), mastery goal orientation (M), and the alienation goal orientation (AA) (11) (12) (24) (25). Going by the pattern observed in loading, it was considered to be an indication that the elements believed to contribute to learning strategy preferences might not be independent paradigms with regard to the goal orientation preferences.

Similarly, the three locus of control survey items loaded in the same manner as the alienation goal orientation and performance goal orientation. With reference to the aims and objectives as well as the questions associated with locus of control and learning strategies that loaded in a similar manner as the goal orientation elements, it was evident that the questions may have failed to differentiate learning strategies from goal orientation or locus of control going by the definitions of the concepts. For this reason, the survey items of the study instrument were considered for transfer to motivation scales which agreed with the item loading patterns for the objectives of this study (11) (24) (25). Moreover, four items of the locus of control preference loaded independently which made it possible to categorise them as either internal locus of control (ILC) or external locus of control (ELC) items.

Chronbach’s Alpha coefficient

The Cronbach’s Alpha coefficient for the study instrument was found to be α = 0.824. Cronbach’s Alpha is a measure of internal consistency or the reliability of a set of test items. In summary, the measure was used to measure the strength of consistency of the observations. Since the score closely approaches 1, there was high covariance among the subscale items.

Mean (average) scores for scales and sub-scales

In determining locus of control (LC), goal orientation (GO) and learning preferences for all study subjects, survey items attributed to each subscale were added together and their mean was taken to obtain the average learning preference subscale score. In this case, the subscale score with the highest average score was taken to be the participants learning preference in that group (see table 2) for both year 1 and year 4 students (11) (25). Also, learning strategy elements were realigned to match their related goal orientation subscales as suggested by the principal factor analysis performed on the study instrument. In summary, this project used the following learning preference scores in analyses: performance goal orientation (P), mastery goal orientation (M), internal locus of control (ILC), alienation to learning goal orientation (AA), and external locus of control (ELC). Also, based on responses from the survey, the mastery goal orientation was identified with students with similar score in performance goal orientation (26) (27). For the 120 year 1 and year 4 students who were included during the administration of modified MAHPMS, results showed that almost three-quarters (74.2 percent) identified with a mastery goal orientation (M) to learning. On the other hand, 25.2 percent identified with or preferred the performance goal orientation (P) to learning. Only 5 students from the total exhibited no dominant or specific learning preference since they had equal scores on the performance and mastery subscales with regard to goal orientation. None of the students demonstrated alienation to learning (AA) goal orientation preference during the study. Table 2 and 3: year 1 students had higher scores in preference of task and causal attribution as compared to year 4 students. Year 4 students scored higher in goal orientation and learning strategies as compared to year 1 students.

These findings may suggest that as students advance through the course, they gradually adopt an internal locus of control which is reflected by the lower “causal attribution” scores for year 4. Also, Year 1 students have slightly higher preference for both difficult and easy tasks than year 4. Although both groups can be considered “neutral” it is evident that year 4 students would not choose difficult tasks with the aim of further learning. Goal orientation and learning strategies scores are largely neutral which may suggest that there is too little inspiration in the course to orchestrate a full transformation to the extreme scores.

The participants were separated by their years of study to obtain descriptive statistics and make inferences with regards to the number of years of study. It was found that both year 1 and year 4 students identified with the mastery goal preferences. However, the preference was not dominant since similar percentage frequency distributions were observed when comparing between performance and mastery goal orientation preferences. This finding was also observed by Cavaco et al. (24). Additionally, for both groups, it was found that none of the students identified with alienation. Perrot et al. (14) obtained a similar finding.

The pattern on the locus of control tests demonstrated that students in both groups had an internal locus of control. As such, both year 1 and year 4 students believed that events in their lives are largely controllable as opposed to the perception that they are controlled by outside forces. A combined analysis showed that the majority (78.6 percent) of this student sample had the internal loci of control. The belief that life events are controlled external forces and are beyond human control; an external locus of control, was held by a minority of the students (20.5 percent). Only 1 participant demonstrated equivalent mean scores in both external and internal locus of control elements. Even when groups were separated according to their years of study, the high incidence of the internal locus of control was persistently observed (70.2 percent for year 1 students and 68.35 percent for year 4 students). As such, the internal locus of control preference frequencies reduced with the increase in the number of years of study.

Multiple regression and Pearson’s r coefficient

For the total study sample (N = 120), stepwise and simultaneous multiple regression was performed using the motivation subscales that had been identified by principal factor analysis as the independent variables. When the five predictor variables were entered at the same time, it was found that they did not produce any statistically significant relationship between the goal orientation scores and the motivation scores (p = 0.332) for all students. When the respondents were assessed by their years of study, it was found that there was no statistically considerable relationship between goal orientations and the predictor variables (p = 0.854). However, eleven year 1 participants indicated a statistically significant association between their goal orientations and combined learning motivation scores when the variables were entered at the same time (p = 0.004). The Pearson r correlation coefficient was found to be 0.802 and R2 = 0.525. This was calculated to investigate the strength of the relationship between two variables.

Stepwise multiple regression entailed addition and/or removal of independent variables one each time based on a statistical formula. It was found that for year 1 students, the alienation to learning goal orientation scores alone had a statistically significant association with preference of task (p= 0.048). However, the strength of the association was found to be very small going by the Pearson correlation coefficient (r = 0.102 and an R2 = 0.023). Therefore, it was not possible to decide on the preferences for difficult and easy tasks (26) (27). The learning motivation scores for year 4 students when examined separately exhibited a similar independent variable- academic alienation, with a statistically significant correlation to preference of task (Pearson r = 0.632, R2 = 0.411, p < 0.001). By using stepwise regression, it was found that none of the relationships between causal attributions and preference of task were statistically significant. On the other hand, when multiple regression analyses were performed, statistically significant results between the learning motivation predictor variables and goal orientation were obtained (Pearson r = 0.582, R2 = 0.299, p = 0.001). When the scores were analysed separately, it emerged that neither year 1 nor year 4 learning strategy subscales scores showed a statistically significant relationship to goal orientation (p = 0.112 and p = 0.080). While their relationship is statistically significant, it is evident that the relative ability of the second variable to predict learning strategies for both groups is small.

Further, the data from all participants was appraised using stepwise multiple regression analysis to obtain the relationship between goal orientations and learning strategies. Mastery goal orientation was entered first and it demonstrated a significant relationship with metacognitive questions (R2 = 0.147, p= 0.001). Additionally, the locus of control scores were added to the mastery goal orientation scores and it was found that there was a slight increase in the strength of the relationship between the two factors (R2 = 0.301, p = 0.021). These significant correlations agree with previous findings in the study by Perrot et al (12). For the year 4 students, one of the motivation subscale scores, the mastery goal orientation, exhibited a statistically significantly correlation with non-cognitive questions when keyed in stepwise (R2 = 0.176, p = 0.008). It is worth noting that the performance goal orientation scores for year 4 students were significantly related with preference for easy task (R2 = 0.211, p = 0.008).

Analysis of variance (ANOVA)

Analysis of variances (ANOVA) of the same independent and dependent variables was performed on goal orientation findings. It was found that only two learning motivation average scores were significantly related to causal attributions or the loci of control. For year 1 participants, students aged 25 years old and beyond identified with the internal locus of control while the younger (<25 years) identified with the external loci of control. The average score for students aged 25 years and above was higher (mean = 3.243) than students aged 24 and lower (mean = 3.001). Regarding the performance goal orientation variable, it emerged that younger students had higher scores which were statistically significant (mean = 2.520) than older students (mean = 2.254).

The next graph compares the means of sub-scales against ethnicity

Ethinicity and sub-scales

The figure above shows the influence of ethnic background/race on motivation among the participants. From the figure above, the Arabs and Indians scored lowly on goal orientation and learning strategies scales. On the contrary, they scored highly in preference of task and casual attribution. These findings suggest that ethnic background of people influences their motivation. Also, it is evident that Africans have an internal loci of control. The wide discrepancies in the various sub-scales in figure 3 emphasises that ethnic backgrounds shape people differently and thereby impact on their inspirations. Interestingly, the performance of the Irish replicates that of the Gypsies which can be attributed to closeness of their backgrounds.

Capability and sub-scales

The figure above shows how the capability status of the body influences motivation. During the empirical investigation, there was a little distinction between “No” and “Unknown” conditions. From the plot, it can be observed that individuals in such categories largely share similar sentiments. Disabled participants scored highly in the following sub-scales: academic alienation, easy task and internal locus of control. The three groups had close results in the following sub-scales: metacognitive and difficult task. Able-bodied participants scored highly in academic alienation which was proven through tests of significance. As such, they do not pay much attention to events surrounding their studies. This is reflected in the “non-cognitive” sub-scale where the low scores suggest that able-bodied participants are unwilling to engage in activities that would enrich their knowledge if such activities do not impact on their grades directly. Lastly, able-bodied participants also scored highly in “avoidance” sub-scale. This suggests that they perform educational obligations when it is absolutely necessary but not for general preparedness.

From the above plot, it can be inferred that capability status of an individual influences their motivation in education. There are major differences in various sub-scales but the overall impression is that disabled students show more concern towards academic achievement than able-bodied students.

Marital status and sub-scales

The figure above illustrates the influence of marriage status on motivation of students. Regarding performance, internal loci of control and avoidance, the means of sub-scales are close. However, there are significant differences in goal orientation and learning strategies scales. Again, the findings suggest that demographics play a huge role in influencing motivation among students. These findings will be subjected to analytical tests of significance for verification.

Summary

In summary, it is evident that demographics play a vital role in influencing motivation among students. Although their learning environments are largely similar, inspirations are shaped by background experiences which in turn shape personality.

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