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## Table of Contents

Background of the study

1.1 Statement of the Problem

1.2. Research questions

1.3 Objective of the study

1.4 Significance of the study

1.5 Limitation of the study

Methodology

2.1 Design of the study

2.2 Population, sampling and sampling techniques

2.3 Data gathering instruments

2.4 Validity and Reliability Test

2.5 Data Analysis Techniques

Data Analysis and Interpretation of the Results

3.1 Background information of the respondents

3.2 Quantitative data analysis of results

3.3 The Regression equation

Discussion

Conclusion

Recommendations

References

## Abstract

*The main objective of the study was to investigate the effect of self regulated learning, academic self-concept and help seeking behavior students’ academic achievement in FCTE. The research design was descriptive survey design with quantitative research approach. The participants of the study were 201 (m=98 and f=103) second year regular students of the college. In selection of the research participants, both probabilistic and non probabilistic sampling techniques were employed. The instrument used to gather the data were questionnaires& document. In addition, to answer the research questions inferential statistics such independent sample t-test, correlation, ANOVA and multiple regression analysis were performed. Furthermore, the beta coefficient analysis was used to determine the direct effects of variables on students’ academic achievement. Based on the analysis of the data, the following major findings were found: The independent samples t-test indicated that there is a significant sex mean difference in, academic self concept, help seeking behavior and academic achievement. As the study revealed, all male college students relatively scored higher mean scores than their female counter parts. But the difference in self regulated learning is not significant. In the ANOVA analysis, in case of academic self concept (ASC), the mean score of social science students obtained was 2.82789, and the mean score of language students was 2.48851 with mean differences 0.339385 . And the difference is statistically significant. And also the mean score of Aesthetics students obtained was 2.83529; the mean score of language students was 2.48851 with mean difference 0.346788 which is statistically significant. Again in the second group of post hoc comparison in case of self regulated learning (SRL), the mean score of language students obtained was 3.90690, and the mean score of professional study students was 3.52727 with mean difference 0.379624, which is statistically significant. Additionally, the mean score of social science students was 3.84592 and the mean score of professional study students was 3.52727 with mean difference 0.318646 , which is statistically significant. Besides the post hoc showed that , the mean score of Aesthetics students was 3.81324, and the mean score of professional study students obtained was 3.52727 with mean difference 0.285963 . This showed that there was a significant difference between the two groups. As the correlation coefficient results demonstrated that there were statistically significant positive correlation between Academic self concept and help seeking behavior and also Academic self concept and academic achievement. Again academic achievement and help seeking behavior correlates positively. But, the significance seen between self regulated learning and academic achievement and self regulated learning and Academic self concept is not that mach significant, The multiple regression analysis showed that SRL ,ASC and HSB variables had a significant composite contribution (R2=0.246, F=56.07, p<0.01).HSB was the first predictor variable entered and it was the most contributors from those other two predictor variables and it was accounted for 0.246 (24.6 % when n=201) the variation in academic achievement. At step two, ASC contributed additional of 7.5 % (when n=201) of the variation on academic achievement and together HSB and ASC contributed 32.1 % of the total prediction. At step three, SRL was entered & accounts for additional 15.6 % of the variation in academic achievement. All three variables HSB, ASC and SRL contributed 47.7 % of the total prediction.*

*Key words: Academic self concept, help seeking behavior, self regulated learning and academic achievement.*

*Alelign Jemberie, Lecturer at Finote Selam college of teachers education*

## Background of the study

When considering academic achievement, in different education levels, among many factors; Self-regulated learning (SRL) is a significant factor. Self-regulated learning refers to one’s ability to under-stand and controls one’s learning environment. Self-regulation abilities include goal setting, self-monitoring, self-instruction, and self-reinforcement. Self-regulation should not be confused with a mental ability or an academic performance skill. Instead, self-regulation is a self-directive process and set of behaviors whereby learners transform their mental abilities into skills and habits through a developmental process that emerges from guided practice and feedback (Paris & Paris, 2001).

Effective learners are self-regulating, analyzing task requirements, setting productive goals, and selecting, adapting or inventing strategies to achieve their objectives. These learners also monitor progress as they work thorough the task, managing disturbing emotions and adjusting strategies processed to foster success. These are the students who ask questions, take notes, and allocate their time and their resources in ways that help them to be in charge of their own learning (Paris & Paris, 2001).

Broadly speaking, SRL students have purposeful and strategic behaviors and learn with a high degree of perseverance; they think about their thinking (meta cognition), plan, monitor, and evaluate their personal progress against a standard, and have an internal motivation to learn Zimmerman, ( 1995) cited in (Zimmerman, Bonnor, & Kovach, 2002). They also have a high degree of self-efficacy, and control their learning environment to stimulate learning to the greatest level possible. Accordingly, students who claim responsibility for their learning and results have a high probability of increasing their capacity to evoke learning experiences stored in their memory, develop their sense of responsibility, and gain independent learning skills. In this case, academic achievements and self-confidence will be raised, and learning goals will be met. In recent years, the concept of SRL has become the focus of applied educational studies as an important variable in boosting academic achievement and bringing about success (Tanriseven and Dilmac, 2013).

Besides to SRL academic self-concept is significant factor in different education level. Academic self-concept can be defined in different literature in different ways. That is academic self-concept refers to a person’s perceptions and knowledge about the self in an academic achievement situation (Low Suet Fin, Zahari Ishak, 2013). Academic self-concept is comprised of a set of attitudes, beliefs, and perceptions held by students about their academic skill sets and performance (Lent, Brown, & Gore, 1997). Academic self concept, according to Cokley (2000), also encompassed a comparative component in which students assessed their academic attitudes and skills in comparison with other students. Academic self-concept has been strongly linked to academic achievement (Marsh, 1990). Also in this research in the context of education has considered academic self-concept as an important psychological construct because it has been found to be both a cause and an effect of academic achievement (Cokley, 2007). A higher academic self-concept has been associated with greater academic achievement among students (Marsh, 1992). Correll, (2001) cited in (Cokley, 2007, there was a statically significant sex difference in male and female students and Men have more confidence in their own abilities than women in many areas, including the evaluation of their own academic abilities, or academic self-concept.

Student’s help seeking behavior is also can be seen as another significant factor in education level like, colleges and universities. Many researchers such as Newman (1998) have shown that help seeking behavior is naturally positive and improve achievement. Those students who have enough knowledge strategies can achieve better than those who do not know when and how to use the learning strategies Newman and Schwager (1992) cited in Kari (2003), indicated that effective help seekers are characterized as acting naturally and purposely, alleviating a real difficulty, learning and monitoring the task at hand and in the end achieving autonomy.

In contrast students who do not know when as a result of in effective monitoring or how (strategy knowledge), or from whom to seek help, are likely to be less successful. Newman & Schwager (1992) cited in Kari (2003) further emphasized that learners always seek help from people around them when they encounter in learning are beyond the explanations of their current knowledge. Seeking help from others allows them to get targeted information exactly when they need it. Compared with other ways of learning, getting help from others is more convenient and less time consuming, and the importance of help seeking cannot be ignored in learning process.

Giving help to students is not always easy nor without some possible negative side effects for students. According to an investigation by Graham and Barker (1990) cited in Elliot (2000), when teachers help students, it can be interpreted as indicating the students lack ability. The help seeking process is closely related to motivational and affection factors There is a large body of research which indicates that peer help, peer tutoring and individual teacher assistance facilitate student achievement (Elliot .2000).

### 1.1 Statement of the Problem

For academic achievement Self-regulated learning (SRL) is a significant factor and also essential to the learning process (Paris & Paris, 2001). It can help students create better learning habits and strengthen their study skills so that apply learning strategies to enhance academic outcomes, monitor their performance, and evaluate their academic progress (Zimmerman, Bonnor, & Kovach, 2002). Teachers thus should be familiar with the factors that influence a learner’s ability to self-regulate and the strategies they can use to identify and promote SRL in their classrooms. SRL is much more difficult to achieve.

Academic self-concept influences students’ later achievement and affects students’ future goals. Students have varying academic self-concepts in various education levels in different subjects (Santrock 1997). Students feel troubled while they get low achievements because of poor academic self concept. They cannot express their ideas in an effective way(Andrew 2005). Some researchers like, Brozovsky, and Mclaughlin (1998) cited in Hogan (2003) noted that there is a need to dig out predictors of students’ academic achievement. In addition, other researchers like recommended more research in area of academic self-concept and also suggested that future research could explore the impact of on students’ academic achievement. Moreover, Parker and Duffy (2005), recommended that more research is required to determine whether or not gender differences do exist in academic self-concept.

Students who ask questions and get help when they need it alleviate immediate learning difficulties and also acquire knowledge and skills that they can use for self help later. Despite the obvious importance of help seeking in the classroom, many students do not ask teachers for help or benefit themselves of when they need it. A study conducted by Alem (2011) showed that help seeking behavior was significantly related with their academic achievement.. In addition the teacher’s teaching (EPSY112) exposure in deferent sections of the college makes him to see these problems frequently. When students are asked to do different assignments with other class room students, they don’t want to ask any help from others. Rather they prefer not to do. And also most of these students have no academic self concept (consider themselves as they are weak and cannot do). Besides to this, the data obtained from FCTE registrar indicates that second year regular students got least academic achievement than the other third year college regular students. According to the researcher’s knowledge there are no studies now in this area on this particular title using these three variables in combination manner (self regulated learning, academic self- concept and help seeking behavior). So the researcher was motivated to conduct the research on FCTE students.

### 1.2. Research questions

To look into the effects of **s** elf regulated learning, academic self concept and help seeking behavior on the academic achievement of students in FCTE, the following basic research questions were formulated.

1) Are there statistically significant mean differences between male and female students on self regulated learning, academic self-concept, help seeking behavior and academic achievement?

2) Are there statistically significant mean differences among different department students on self regulated learning, academic self-concept, help seeking behavior?

3) Are there significant relationships between self regulated learning, academic self- concept, help seeking behavior and academic achievement?

4) To what extent self regulated learning academic self-concept and help seeking behavior predict students’ academic achievement?

### 1.3 Objective of the study

The general objective of the study was to investigate the effects of self regulated learning **,** academic self- concept and help seeking behavior on students’ academic achievement in FCTE. More specifically, this study was conducted:-

1) To Examine mean difference between male and female students on self regulated learning, academic self-concept, help seeking behavior and academic achievement.

2) To assess significant mean differences among department students on self regulated learning, academic self-concept, help seeking behavior.

3) To Examine significant relationships between self regulated learning, academic self- concept, help seeking behavior and academic achievement

4) To Investigate whether self regulated learning **,** academic self-concept and help seeking behavior variables predict significantly students’ academic achievement or not

### 1.4 Significance of the study

This study was expected to have the following contributions.

a) Trainees will be aware of the effect of their own self regulated learning **,** academic self concept and help seeking behavior beliefs on their own academic achievement success or failure and play for the improvement of their own academic achievement success.

b) The study will serve as an input for the college departments to understand that their trainees have different self regulated learning, academic self concept and help seeking behavior, so that, they can treat them according to their needs and interest at college by giving different advices and guidance.

c) It will have significance for instructors to expand their theoretical background in the area self regulated learning **,** academic self concept and help seeking behavior.

### 1.5 Limitation of the study

The following limitations should be taken in to considerations while using the findings of the study. The first limitation is that, some factors that may have an impact on academic achievement were not controlled while testing the effect of ASC, SRL and HSB on students’ academic achievement. Secondly, the samples were taken from one geographical area which may be results do not generalize. Thirdly, besides to above, all departments should have been treated separately rather than merging in to five departments. And finally second year extension students should have been considered.

## Methodology

### 2.1 Design of the study

The research design to fulfill the purpose of this study was mainly descriptive survey design because this design will examine the effects and relationships between variables of the study (self regulated learning, academic self concept and help seeking behavior on academic achievement) (Gay and Airasian, 2000: Smoekh & Lewin, 2005). Again Quantitative research approach was also used to see the effects of self regulated learning , academic self concept and help seeking behavior on students’ academic achievement by gathering quantitative data through quantitative data collecting instruments (Dessalegn, 2000).

### 2.2 Population, sampling and sampling techniques

The study was conducted in Finote Selam town, in 2018/2019 G.C on purposefully selected second year regular college students. The population of second year regular students in the college is 480 (225 male and 255 females). In selection of the research participants, both probabilistic (simple random) and non probabilistic sampling (purposive sampling) techniques were employed. The college and second year (batch) students were selected as a sample through purposive sampling method. The reason behind the selection of Finote Selam college was mainly because, the researcher’s familiarity with college and town, which facilitates the researcher’s communication with college students and reduce confusion which are usually resulted from being new to the area cultures and traditions.

Besides the reason why Second year students selected is that the experience in teaching (Epsy112) these students made me to focus on. Simple random sampling technique was also applied to select sample students from 5 merged departments of 480 second year regular students. The reason why Simple random sampling is that, because it gives equal chance to select samples (Fraenkel and Wallen, 2008). To determine samples size from the total population, Yemanes’ formula, n= N/1+N (e) 2 was used. n= 480/1+480(0.05)2, n= 218. Where, n = sample size, N= total population, and e =error which is 0.05. And all students are 218 (45.4%) from the total 480 population. So this was taken as samples of this study. From total number of second year students of the college (480) the sample size is 218 (102 males and 116 females). To select samples from all 5 departments sampling proportionality was used (k = 0.454)

**Table 3.3.1: population and samples taken from departments **

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**Note**: M = Male, F = Female, T = Total, N = Number of samples

### 2.3 Data gathering instruments

Questionnaire and document were used to collect information from the participants of the study. Three separate scale questionnaires were administered to the participants to collect data about their SRL, academic self concept and help seeking behavior.

The questionnaires had three parts: part one was about participants’ personal profile that is about sex, roll number of the students. Sex was included in order to see sex difference on SRL, academic self concept, help seeking behavior and academic achievement of the students. In addition; roll numbers of students was incorporated to obtain their CGPA from the college registrar office easily as a measurement of academic achievement.

### 2.4 Validity and Reliability Test

To monitor validity, the instruments were examined by experts of professional department teachers in Finote Selam College. Following that the necessary amendments were made. Again pilot study was conducted before the main study to assess the reliability of the instrument with 30 students in the study area and reliability of the questionnaire was monitored.

*Table 3.5.1: Reliability Statistics of the Instruments*

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- **Self regulated learning instrument items** were adapted from Ahmad (2007). The instrument contains 20 items with five point Likert scale (1=strongly disagree, 2= disagree, 3= I don’t decide, 4=agree, 5= strongly agree).

- ** Scales for academic self concept** were adapted from Marsh (1992), to assess academic self concept. The instrument contains 30 items with five point Likert scale (1=I strongly disagree, 2= I disagree, 3= I don’t decide, 4=I agree, 5= I strongly agree.

- **Scales help seeking behavior** instrument contains 14 items with five point likert scale (1=strongly disagree, 2= disagree, 3= moderately agree, 4=agree, 5= strongly agree). The items were adopted from Alem (2011).

- **Students’ academic achievement (DA)** second semester (2010 E.C) GPA results of the sampled students were taken from second semester registrar of the sampled students.

### 2.5 Data Analysis Techniques

For the purpose of this study different inferential statistical techniques such as independent two sample test (for Q1), (for Q2), correlation (for Q3) and multiple regressions (for Q 4) were used to address the research questions.

## Data Analysis and Interpretation of the Results

This chapter presents the analysis and results of the study. It followed a thematic analysis and interpretation approach as it is preferable to give readers a comprehensive and continuous picture of the whole study.

### 3.1 Background information of the respondents

The background information of participants is presented under her.

*Table 4.1: Background information of students and the departments they represent*

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**Note**: M = Male, F = Female, T = Total, N = Number of samples

As indicated above, in table 4.1, the background information of second year regular students in accordance with their sex and their department. Out of 201 students, about 98 (48.8%) were males, and 103 (51.2 %) of them were females. Again in accordance with their department out of 5 merged departments, 32.83% were Professional courses, 24.34% were Math & N/science, 14.4% were Language, 11.44% were Social studies, and 16.9% were Aesthetics trainees.

### 3.2 Quantitative data analysis of results

The main purpose of this study was to investigate effects of self regulated learning, academic self-concept and help seeking behavior on students’ academic achievement. This study used quantitative data analysis techniques based on the nature of research question.

**The significant mean differences between male and female students on self regulated learning, academic self-concept, help seeking behavior and academic achievement (Q 1)**

The first research question was intended to answer about the mean differences of SRL, ASC, HSB, and AA, exist between male and female using sex variable. To answer this research question, independent sample t-test was conducted based on its assumption on table 4.3.3. And the results are presented in Table 4. 2.

*Table 4.2: Descriptive statistics and Summary of independent samples t- test of Sex differences*

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** Significant P< 0.05, df = 199, two tailed*

The above table 4.2 also indicated that, the mean score of male students in Self regulated learning was 3.77857and the mean score female students was 3.67670 with mean difference 0.1025. Again, the mean score of male students in academic self concept was 2.82959 and the mean score female academic self concept was 2.61715 with mean difference 0.2124. Besides, the mean score of male students in help seeking behavior was 4.15743 and the mean score females in help seeking behavior was 3.80791with mean difference 0.3495. In addition, male students’ mean score of academic achievement was also greater than female students’ with mean difference 0.5397. In addition, the above table 4.2 results of the two samples t-test indicated that there is a significant sex mean difference in, academic self concept, help seeking behavior and academic achievement. As the study revealed, all male college students relatively scored higher mean scores than their female counter parts. In academic self concept (t-obtained 3.108), help seeking behavior (t-obtained -4.235) and academic achievement (t-obtained -7.377), (p<0.05). But the difference in self regulated learning at (t-obtained -1.621,(p>0.05 ) is not significant.

**The significant mean differences among different departments on self regulated learning, academic self-concept and help seeking behavior on student’s academic achievement (Q 2)**

Here an attempt was made to check whether there are significant mean differences among different department students on HSB, ASC and SRL or not **.** Here, five departments of college students were treated by one way ANOVA and post hoc test was used. But, to do inferential statistics like ANOVA, first of all assumption of normally must be cheeked. So, the researcher attempted to check the assumption of ANOVA as it presented on table 4.4.3. Then ANOVA followed.

On help seeking behavior (HSB) the mean scores of Aesthetics was 4.20168 , the mean scores of language was 3.96305, the mean score of professional studies was 3.97186 , the mean score of social science was 3.90671 and the mean of Math &E was 3.83851. So, Aesthetics students had the largest mean score (4.20168) but, Math &E students had the smallest mean (3.83851) on the HSB. Again On the academic self concept variable (ASC), the mean scores of Aesthetics was 2.83529, the mean scores of language was 2.48851, the mean score of professional studies was 2.70354, the mean score of social science was 2.82789 and the mean of Math &E was 2.66522. So, Aesthetics students had the largest mean score (2.83529) but, language students had the smallest mean score (2.48851) on the ASC.

In addition, on self regulated learning (SRL), the mean scores of language was 3.90690, the mean score of social science was 3.84592, the mean scores of Aesthetics was 3.81324, the mean of Math &E was 3.68696, and the mean score of professional studies was 3.52727. So, language students had the largest mean score (3.90690) but, professional study students had the smallest mean score (3.52727) on the SRL.

*Table 4.3:Summary of One way ANOVA for variables*

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* **Significant at a = 0.05 **

the above ANOVA summary table (table 4.3) showed that academic self concept and self regulated learning had a significant effect on students’ academic achievement [F (4,196) =2.818, p<0.05] and [F (4,196) =6.293, p<0.05] respectively .But the ANOVA test above indicated that, HSB is not significant at p<0.05, which means it had no significant effect on students’ academic achievement. Post hoc test was designed for this situation because the researcher had already obtained a significant difference of F-test on two groups. Additional exploration of the differences among mean scores is needed to provide specific information on which means are significantly different from each other. So the next step was to use post hoc test to get the significant difference between groups as showed below.

*Table 4.4: Post hoc multiple comparison for variables (ASC, SRL)*

*Table 4.4.1: Post hoc on academic self concept (ASC)*

Tukey HSD: (honestly significance difference)

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** *The mean difference is significant at p< 0.05 alpha level**

* Table 4.4.2: Post hoc on self regulated learning (SRL)*

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*** The mean difference is significant at p< 0.05 alpha level**

As it is indicated on the above post hoc comparison table 4.4, the significance differences of students’ mean score among ASC and SRL were shown separately below:

In the first group, on table 4.4.1 in case of academic self concept (ASC), the mean score of social science students was obtained 2.82789, and the mean score of language students was 2.48851 with mean difference 0.339385 **. A** nd the difference is statistically significant. The difference was in favor of social science students. And also the mean score of Aesthetics students obtained was 2.83529, and the mean score of language students was 2.48851 with mean difference 0.346788 which is statistically significant. The difference was in favor of Aesthetics students.

Again in the second group of post hoc comparison table 4.6.2, in case of self regulated learning (SRL), the mean score of language students obtained was 3.90690, and the mean score of professional study students was 3.52727 with mean difference 0.379624, which is statistically significant. The difference was in favor of language students. Additionally it is shown that, the mean score of social science students was 3.84592 and the mean score of professional study students was 3.52727 with mean difference 0.318646 **,** which is statistically significant. The difference was in favor of social science students. Finally, the post hoc showed that **,** the mean score of Aesthetics students was 3.81324, and the mean score of professional study students obtained was 3.52727 with mean difference 0.285963 **.** This showed that there was a significant difference between the two groups. The difference was in favor Aesthetics department students.

**The significant relationships between self regulated learning, academic self- concept, help seeking behavior and academic achievement (Q3)**

To examine the relation between these three variables, first it was important to verify how self regulated learning, academic self- concept, help seeking behavior and academic achievement scores are quantified. A set of 20 self regulated learning, 30 academic self- concept and 14 help seeking behavior totally 64 questionnaires were administered to 201 students to get information. Every student filled 64 likert type items ranging from 1-5 (1=I strongly disagree, 2= I disagree, 3= I don’t decide, 4=I agree, 5= I strongly agree) and had his or her Owen average score. Again, every student has average results (GPA) of one year. After this an attempt was made to determine the overall relationship between HSB, ASC, SRL and AA using Pearson product moment correlation coefficient. To check the normal distribution of the data the Kolmogorov-Smirnov test statistics shown in below table 4.4.3 is used.

*Table 4.4.3 Normality Test to check the normal distribution*

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To conclude that the data came from the normal distribution, the P-value should be found less than 0.05. Another assumption about normality test value is that, when the obtained statistics is found between -1.92 and 1.92 the distribution is normal. Based on this assumption, the obtained statistical value in table 4.4.3 above showed that the distribution is normal and accepted to use correlation and regression. Accordingly, the results are presented in Table 4.4.4 below.

**Table 4.4.4: a summary for correlation between ASC, SRL, HSB and AA.**

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** *Correlation is significant at the 0.01 level (2-tailed*

As indicated in Table 4.4.4 above, the correlation coefficient results demonstrated that there were statistically significant positive correlation between academic self concept and help seeking behavior (r=0.454, N=201, p<0.01), and also Academic self concept and academic achievement(r=0.293, N=201, p<0.01), again academic achievement and help seeking behavior correlates positively (r=0.469, N=201, p<0.01). But, the significance seen between self regulated learning and academic achievement(r=0.117, N=201, p<0.01), self regulated learning and Academic self concept ((r=0.108, N=201, p<0.01), not that mach significant, it had very weak positive relationship between them.

**The extent self regulated, learning academic self-concept and help seeking behavior predict academic achievement (Q 4)**

**The multi-collinearity test of independent variable**

In a research to apply multiple regression analysis, it is always expected that a predictor variable selected by the researcher must be correlated with the criterion variable, but that is not strongly correlated with any other predictor variable. The term multi-colinearity is used to describe the situation when a high correlation is detected between two or more predictor variables. Such high correlations cause problems when trying to draw inferences about the relative contribution of each predictor variable to the success of the model (Montgomery,Vining, and Peck, 2006). So, the researcher needs to check and ensure that the assumption of multi-collinearity had not been violated by having any variable that was too closely related to another by checking the Pearson correlation coefficient (R), the tolerance level (T) and the variance inflation factor (VIF) among the predictive variables.

The absence of multi-colliniarity test is precondition for regression analysis. After calculating the multi Collinearity test, the researcher tried to discuss with the assumption. The general rule of thumb states that when a variance inflation factor (VIF) of a given predictor variable is less than five (VIF<5), the level of tolerance (T) is greater than or equal to zero point two (T≥0.2), and its correlation coefficient(r) with other predictor variable is less than zero point eight (R< 0.8), the predictor variable has no multi-collinearity problem. Hence, assessment of multi-collinearity among the below mentioned three predictor variables did not violet this research. This is because, neither the tolerance, correlation coefficient (R) nor the variance inflation factor (VIF) indicated on table 4.4.5 below showed a significant presence of multi-co linearity.

**Table 4.4.5: multi-colliniarity test of independent variable**

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**Note: VIF- variance inflation factor**

The forth purpose of this study was to examine whether self regulated learning, academic self concept and help seeking behavior significantly predict college students’ academic achievement or not. To check this, multiple linear regression analysis was performed and the output is discussed under here.

**Table 4.4.6 Multiple Linear Regression Analysis of three predictor (X1 -X3) Variables on the effectiveness of college students’ academic achievement(Y)**

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### 3.3 The Regression equation

Y (Academic achievement) =1.500 +0.404X1+0.151X2-0.158X3

As it was stated by Landauand Everitt (2004), applying multiple regression analysis to a set of data results in what are known as regression coefficients, one for each explanatory variable. These coefficients give the estimated change in the response / dependant / variable associated with a unit change in the corresponding explanatory /independent variable, considering the other explanatory variables remaining constant.

Taking the regression equation demonstrated above into account, we can make the following statements. The first number in the regression equation is 1.500. This is the constant. This means that when the values of all the three selected predictor variables become zero, the value of the dependent variable (academic achievement) will be 1.500. Similarly, the regression coefficient of X1 is 0.404 (i.e. the slop of the line).This implies that keeping other variables being constant, a unit increase in help seeking behavior (X1) increase the value of the dependent variable (AA) by 0.404. The regression coefficient of X2 is 0.151 (i.e. the slop of the line).This implies that keeping other variables being constant, a unit increase in academic self concept (X2) increase the value of the dependent variable (AA) by 0.151. Again the regression coefficient of X3 is -0.158 (i.e. the slop of the line).This implies that keeping other variables being constant, a unit decrease in self regulated learning (X3) decreases the value of the dependent variable (AA) by -0.158. The focus of this research is to determine the individual and combined effect of the three predictor variables. Hence, first it is important to analyze the independent contribution of each potential predictor variable, whether they are statistically significant. For doing such analysis, the regression coefficients, and the partial t-test were used with degree of freedom (df=199) and level of significance (p<0.05).

On the above table 4.4.6 under un Standardized Coefficients B , Help seeking behavior (X1, B= *0.404*) is statistically significant (p=0.000), and the regression coefficient is positive which would indicate that high level of help seeking behavior which is related to increase academic achievement of college students(DV). In the same way, self regulated learning (X3, B= -0.158), has statistically significant (p=0.027) negative relationship with the dependant variable (academic achievement =Y). But Academic self concept has positive relationship with no significance (X2, B = 0.151, p=0.071). Here, we can see that the two treated variables X1 and X3, had statistically predicted the DV which is academic achievement.

Again in the same table 4.4.6 above standardized Coefficients beta (β), the β value is a measure of how strongly each predictor variable influences the criterion variable. The β is measured in units of standard deviation. When we have more than one predictor variable, the β regression coefficient is computed to allow us to make such comparisons and to assess the strength of the relationship between each predictor variable (X1-X3) with the DV variable(Y). From the SPSS output, the researcher found out that help seeking behavior (X1, β= 0.422, p=000) was significant predictors of the criterion variable. On the other hand, the researcher found that academic self concept(X2) and Self regulated learning (X3) has the effect on DV next to HSB (X2, β= 0.128, p=0.071 ) and (X3 β= -0.141, P=0.027) respectively.

As the ANOVA summary in Table 4.4.7 indicated below, the model is fit to predict the academic achievement from help seeking behavior, academic self concept and self regulated learning as it was found statistically significant (*[df=1, 199; F=56.07, p<0.01]*)

*Table 4.4.7 One way Analysis of Variance (ANOVA) of the Regression Model*

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The SPSS output also labeled on ANOVA for analysis, at (table 4.4.7) above. It provided results for a test of significance for R, R[2] and adjusted R[2] using the F-statistic. In this analysis, the *p* -value is well below 0.05 (p=0.000) at degree of freedom (df=1,199) and (F=56.077). Therefore, it can be concluded that R, R[2] and adjusted R[2] that exist between the predictor variables and the criterion variable is statistically significant and there is a relationship between the independent variables and the dependent variable.

*Table 4.4.8 Model Summary of Regression*

Abbildung in dieser Leseprobe nicht enthalten

The other output in multiple regression analysis is the model summary with all possible predictor variables included. To construct the model, all predictor variables are included in the first block and the "Method" remains on the default value of "Enter". This is because the researcher wanted to see the cumulative effect of the predictor variables on the criterion variable. From the model summary table with the R, R[2],, and *ad* R[2]. R **(** 0.691) is a measure of the correlation between the observed value and the predicted value of the criterion variable. In our case, this would be the correlation between the three IVs and DV. R square (R[2]) is the square of this measure of correlation and indicates the proportion of the variance in the criterion variable which is accounted by the model. In this case, the effectiveness of academic achievement was accounted by the predictors (X1, X2, and X3).

One can found from the same table that the unadjusted multiple R[2] for this data is 0.477, and the adjusted multiple R[2] is 0.433. Here, the three predictor variables together accounted about 0.477 (47.7%) of the variance on the value of the dependent variable (academic achievement).

*Table 4.4.9 Stepwise Multiple Linear Regression to calculate high Predictive Value of the independent variable on Dependent Variable*

Abbildung in dieser Leseprobe nicht enthalten

At step two, ASC was entered in to the regression equation and it was contributed additional of 7.5 % (when n=201) of the variation from academic achievement and together HSB and ASC contributed 32.1 % of the total prediction. Therefore, to predict academic achievement using these two independent variable HSB and ASC the following formula used. AA (Y) =0.404 (HSB) + 0.151 (ASC)+ constant .

At step three, SRL was entered in to the regression equation and it accounts for additional 15.6 % of the variation in academic achievement. All three variables HSB, ASC and SRL contributed 47.7 % of the total prediction. Therefore to predict academic achievement using these three independent variables the following formula used is AA (Y) =0.404 (HSB) + 0.151 (ASC) -0.158 (SRL) + constant

## Discussion

In this section, the findings of the study reported in result section were discussed thoroughly in line with the findings of other research works.

**1.Results of independent sample t-test of mean differences between male and female students on self regulated learning, academic self-concept, help seeking behavior and academic achievement (Q 1)**

The results from this study confirmed that there were no significant differences between male and female participants on the self regulated learning at (t-obtained -1.621,(p>0.05 ). As regard to sex differences in academic self concept the result of the current study also indicated that, there was a statistically significant sex differences in male students and female students(t-obtained 3.108), (p,0.05). Consistently to the current result (Cokley (2000) conducted that there was a statically significant sex difference in male and female students and Men have more confidence in their own abilities than women in many areas, including the evaluation of their own academic abilities, or academic self-concept.

Again regarding HSB the result of this study also indicated that, there was a statistically significant sex differences in male students and female students at (t-obtained -4.235), (p<0.05). At the same way William (2011) stated, his study that there is significant sex difference between male and female students on help seeking. Again In this perspective local study, for example, Dawit (2008), cited in Tadesse (2011), showed that there is significant difference between male and female students’ in help seeking performance. On the contrary, Kabtamu (2007) stated in his research findings explained that no statistically significant difference was obtained in help seeking behavior between male and female students.

Additionally the two sample t-test analysis showed statistically significant sex differences in academic achievement at ( t-obtained -7.377) , (p<0.05), which the mean score for male student was higher than the female students, consistently the study conducted by Singh and Singh (2009) and Sunbul and Aslan (2007) revealed significant differences in academic achievement of boys and girls. Moreover, a locally conducted by Tadesse (2011) showed that there was a gender difference in the academic achievement of male and female students. In contrast to this, in other studies, for example, Naderi (2009), no significant differences between GPA and gender was observed. In addition, Farook (2003) founded that the comparison of both gender on academic revealed no significant differences.

**2. Results of ANOVA mean differences among different departments on self regulated learning, academic self-concept and help seeking behavior on student’s academic achievement (Q 2)**

Here an attempt was made to check mean differences among different departments on self regulated learning, academic self-concept and help seeking behavior on student’s academic achievement **.** On the academic self concept variable (ASC), the mean scores of Aesthetics was 2.83529, the mean scores of language was 2.48851, the mean score of professional studies was 2.70354, the mean score of social science was 2.82789 and the mean of Math &E was 2.66522. So, Aesthetics students had the largest mean score (2.83529) but, language students had the smallest mean score (2.48851) on the ASC. In addition, the mean score differences on self regulated learning (SRL), the mean scores of language was 3.90690, the mean score of social science was 3.84592 the mean scores of Aesthetics was 3.81324, the mean of Math &E was 3.68696, and the mean score of professional studies was 3.52727. So, language students had the largest mean score (3.90690) but, professional study students had the smallest mean score (3.52727) on the SRL.

In the above ANOVA summary table (table 4.4) showed that academic self concept and self regulated learning had a significant effect on students’ academic achievement [F (4,196) =2.818, p<0.05] and [F (4,196) =6.293, p<0.05] respectively .But the ANOVA test above indicated that, HSB is not significant at p<0.05, which means it had no significant effect on students’ academic achievement.

As it is indicated on the above post hoc comparison table 4.5, the significance differences of students’ mean score among ASC and SRL: in case of academic self concept (ASC), the mean score of social science students was obtained 2.82789, and the mean score of language students was 2.48851 with mean difference 0.339385 **. A** nd the difference is statistically significant. The difference was in favor of social science students. And also the mean score of Aesthetics students obtained was 2.83529; the mean score of language students was 2.48851 with mean difference 0.346788 which is statistically significant. The difference was in favor of Aesthetics students.

Again in case of self regulated learning (SRL), the mean score of language students obtained was 3.90690, and the mean score of professional study students was 3.52727 with mean difference 0.379624, which is statistically significant. The difference was in favor of language students. Additionally it is shown that, the mean score of social science students was 3.84592 and the mean score of professional study students was 3.52727 with mean difference 0.318646 **,** which is statistically significant. The difference was in favor of social science students. Besides the post hoc showed that **,** the mean score of Aesthetics students was 3.81324, and the mean score of professional study students obtained was 3.52727 with mean difference 0.285963 **.** This showed that there was a significant difference between the two groups. The difference was in favor Aesthetics department students. This finding was supported by some study reports of literature, When considering academic achievement, in different education levels, among many factors; Self-regulated learning (SRL) is a significant factor (Paris & Paris, 2001). Self-regulation is essential to the learning process. Academic self-concept also influences students’ later achievement and affects students’ future goals. Students have varying academic self-concepts in various education levels in different subjects (Santrock 1997).

**3. Results of correlation between self regulated learning, academic self- concept, help seeking behavior and academic achievement (Q3)**

The main purpose of this topic was to present the discussed results about the relationship between **self** regulated learning, academic self- concept, help seeking behavior and academic achievement?. Therefore, an attempt was made to show the overall relationship between ASC, HSB, and students’ AA. As indicated in Table 4.6 above, The current study the Pearson product moment correlation analysis reported that there were statistically significant positive correlation between Academic self concept and academic achievement(r=0.293, N=201, p<0.01). This finding was supported by some study reports that academic self concept and academic achievement are correlated. For example, Dambudzo (2005) with this regard find out that there was a significant and positive relationship between academic self concept and academic achievement.

Another study similar to the previous one reports that academic self concept proves itself favorable associated with academic achievement (Sanchez & Roda, 2004).The possible relationship between academic self concept and academic achievement in this study also agree with the findings of Marsh and Parker (1997) obtained that academic self concept was found to be correlated positively and significantly significant with academic achievement. However, this analysis indicated that, the significance seen between self regulated learning and academic achievement(r=0.117, N=201, p<0.01), self regulated learning and Academic self concept ((r=0.108, N=201, p<0.01), not that mach significant, it had very weak positive relationship between them. Again academic achievement and help seeking behavior correlates positively (r=0.469, N=201, p<0.01). this finding was consistent with other findings such as , Pintrich et.al., (1991); Newman, (1994) cited in Kari (2003), there is a large body of research which indicates that peer help, peer tutoring and individual teacher assistance facilitate student’s academic achievements. Students can learn to manage the support of others. They can seek help from their peers or from their teachers. High achievers have been found to engage in help seeking from their teachers or classmates relatively frequently. Similarly Elliot (2000) indicated that students who ask questions and get help when they need it alleviate immediate learning difficulties and also acquire knowledge and skill that they can use for self help later.

**4. Results of multiple regression of self regulated, learning academic self-concept and help seeking behavior on academic achievement (Q 4)**

From the stepwise analysis of table 4.10 above HSB, HSB and ASC were the variables entered in to the regression equation and they were found the significant contributors of the criterion variable AA .

HSB was the first predictor variable entered and it was the most contributors from those other two predictor variables and it was accounted for 0.246 (26.7% when n=201) the variation in academic achievement. This finding was consistently with the previous research findings, shown that help seeking behavior is naturally positive and improve academic achievement. Related to this point Newman (1994) cited in Kari, (2003) stated that help seeking process is closely related to motivational and affection factors. Researchers indicate that peer help, peer tutoring and individual teacher assistance facilitate students’ academic achievement.

At step two, ASC was entered in to the regression equation and it was contributed additional of 7.5% (when n=201) of the variation from academic achievement and together HSB and ASC contributed 32.1 % of the total prediction. At step three, SRL was entered in to the regression equation and it accounts for additional 15.6 % of the variation in academic achievement. All three variables HSB, ASC and SRL contributed 47.7 % of the total prediction. Research finding of Wondimu and Marjon, (2006) indicates that academic self-concept significantly predict student’s academic achievement. According to researchers, students who felt positively about themselves in life will be more competent in specific domains such as academics. This is consistent with Mendez (2006) study which shows that students with high academic self-concept will have a cumulative GPA higher than those with low academic self-concept

## Conclusion

Based on the findings and discussions made in this study the following conclusions were made.

The result of the study showed that there were a significant mean score differences in ASC, HSB, SRL and AA, between male and female students of the college. In relation to this, male students have relatively higher levels of ASC, HSB, SRL and AA in the college. From this, we can see that male students have high academic performance than female students.

In the ANOVA analysis, in academic self concept (ASC), the mean score of social science students was greater than the mean score of language students by 0.339385 **.** And the difference is statistically significant. And also the mean score of Aesthetics students obtained greater than the mean score of language students by 0.346788 which is statistically significant.

In case of self regulated learning (SRL), the mean score of language students obtained was greater than the mean score of professional study students by 0.379624, which is statistically significant. Additionally, the mean score of social science students was greater than the mean score of professional study students by 0.318646 **,** which is statistically significant. Besides, the mean score of Aesthetics students was greater than the mean score of professional study students by 0.285963 **.** This showed that there was a significant difference between the two groups.

The result of the study also showed that there were statistically significant positive correlation between Academic self concept and help seeking behavior and also Academic self concept and academic achievement. Again academic achievement and help seeking behavior correlates positively. Therefore, students having high academic self concept and help seeking behavior in turn will have high academic performance. From the findings, one can conclude that academic self concept and help seeking behavior predict students’ academic achievement on the other hand the significance seen between self regulated learning and academic achievement is not that mach significant. Also the implication of these finding is that there is a need for promoting students’ SRL through training. The result of the study again showed that all three independent variables (HSB, ASC and SRL) contributed 47.7 % of the total prediction on academic achievement.

## Recommendations

- Female trainees were low in ASC, HSB, SRL and AA as compared with male trainees in the college. Thus, instructors and college administrators should take considerable measures toward enhancing the academic achievement of these trainees by making interventions, designating special programs

- Results revealed by ANOVA showed that in case of SRL, the mean score of professional study students are relatively lower than language, Social and Aesthetics department students. And also, in case of ASC **,** the mean score of language students are relatively lower than other Social and Aesthetics students. Therefore the concerned departments, the colleges and counseling officer should design an intervention programs on ASC and SRL (e.g. Panel discussion, workshop, different tutorials) etc that helps to improve the students SRL and ASC and then improve their academic achievements.

- Results showed that ASC, HSB and SRL predicted academic achievement of students. Therefore, college instructors, college administers and counselors should work hard to develop the trainees’ ASC,HSB and SRL by counseling and training them.

- Further study with a large sample and wider geographical area should be conducted on the effects of ASC, HSB and SRL on trainees’ academic achievement to reach a reliable conclusion.

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- Alelign Jemberie (Author), 2020, Effects of Self Regulated Learning, Academic Self-Concept and Help-Seeking Behaviour on Students’ Academic Achievement, Munich, GRIN Verlag, https://www.grin.com/document/537777

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