Data analysis for fraud detection

Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes.

In general, the primary reason to use data analytics techniques is to tackle fraud since many internal control systems have serious weaknesses. For example, the currently prevailing approach employed by many law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of fraud cases remain undetected and unprosecuted. In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, the sounds like function, regression analysis, clustering analysis, and gap analysis. Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence.

Statistical techniques

Examples of statistical data analysis techniques are:

• Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data.
• Calculation of various statistical parameters such as averages, quantiles, performance metrics, probability distributions, and so on. For example, the averages may include average length of call, average number of calls per month and average delays in bill payment.
• Models and probability distributions of various business activities either in terms of various parameters or probability distributions.
• Computing user profiles.
• Time-series analysis of time-dependent data.
• Clustering and classification to find patterns and associations among groups of data.
• Data matching Data matching is used to compare two sets of collected data. The process can be performed based on algorithms or programmed loops. Trying to match sets of data against each other or comparing complex data types. Data matching is used to remove duplicate records and identify links between two data sets for marketing, security or other uses.
• Sounds like Function is used to find values that sound similar. The Phonetic similarity is one way to locate possible duplicate values, or inconsistent spelling in manually entered data. The ‘sounds like’ function converts the comparison strings to four-character American Soundex codes, which are based on the first letter, and the first three consonants after the first letter, in each string.
• Regression analysis allows you to examine the relationship between two or more variables of interest. Regression analysis estimates relationships between independent variables and a dependent variable. This method can be used to help understand and identify relationships among variables and predict actual results.
• Gap analysis is used to determine whether business requirements are being met, if not, what are the steps that should be taken to meet successfully.
• Matching algorithms to detect anomalies in the behavior of transactions or users as compared to previously known models and profiles. Techniques are also needed to eliminate false alarms, estimate risks, and predict future of current transactions or users.

Some forensic accountants specialize in forensic analytics which is the procurement and analysis of electronic data to reconstruct, detect, or otherwise support a claim of financial fraud. The main steps in forensic analytics are data collection, data preparation, data analysis, and reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use.

Artificial intelligence

Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include:

• Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
• Expert systems to encode expertise for detecting fraud in the form of rules.
• Pattern recognition to detect approximate classes, clusters, or patterns of suspicious behavior either automatically (unsupervised) or to match given inputs.
• Machine learning techniques to automatically identify characteristics of fraud.
• Neural nets to independently generate classification, clustering, generalization, and forecasting that can then be compared against conclusions raised in internal audits or formal financial documents such as 10-Q.

Other techniques such as link analysis, Bayesian networks, decision theory, and sequence matching are also used for fraud detection. A new and novel technique called System properties approach has also been employed where ever rank data is available.

Statistical analysis of research data is the most comprehensive method for determining if data fraud exists. Data fraud as defined by the Office of Research Integrity (ORI) includes fabrication, falsification and plagiarism.

Machine learning and data mining

Main articles: Machine learning and Data mining

Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations and can help to get better insights into the processes behind the data. Although the traditional data analysis techniques can indirectly lead us to knowledge, it is still created by human analysts.

To go beyond, a data analysis system has to be equipped with a substantial amount of background knowledge, and be able to perform reasoning tasks involving that knowledge and the data provided. In effort to meet this goal, researchers have turned to ideas from the machine learning field. This is a natural source of ideas, since the machine learning task can be described as turning background knowledge and examples (input) into knowledge (output).

If data mining results in discovering meaningful patterns, data turns into information. Information or patterns that are novel, valid and potentially useful are not merely information, but knowledge. One speaks of discovering knowledge, before hidden in the huge amount of data, but now revealed.

The machine learning and artificial intelligence solutions may be classified into two categories: 'supervised' and 'unsupervised' learning. These methods seek for accounts, customers, suppliers, etc. that behave 'unusually' in order to output suspicion scores, rules or visual anomalies, depending on the method.

Whether supervised or unsupervised methods are used, note that the output gives us only an indication of fraud likelihood. No stand alone statistical analysis can assure that a particular object is a fraudulent one, but they can identify them with very high degrees of accuracy. As a result, effective collaboration between machine learning model and human analysts is vital to the success of fraud detection applications.

Supervised learning

Main article: Supervised learning

In supervised learning, a random sub-sample of all records is taken and manually classified as either 'fraudulent' or 'non-fraudulent' (task can be decomposed on more classes to meet algorithm requirements). Relatively rare events such as fraud may need to be over sampled to get a big enough sample size. These manually classified records are then used to train a supervised machine learning algorithm. After building a model using this training data, the algorithm should be able to classify new records as either fraudulent or non-fraudulent.

Supervised neural networks, fuzzy neural nets, and combinations of neural nets and rules, have been extensively explored and used for detecting fraud in mobile phone networks and financial statement fraud.

Bayesian learning neural network is implemented for credit card fraud detection, telecommunications fraud, auto claim fraud detection, and medical insurance fraud.

Hybrid knowledge/statistical-based systems, where expert knowledge is integrated with statistical power, use a series of data mining techniques for the purpose of detecting cellular clone fraud. Specifically, a rule-learning program to uncover indicators of fraudulent behaviour from a large database of customer transactions is implemented.

Cahill et al. (2000) design a fraud signature, based on data of fraudulent calls, to detect telecommunications fraud. For scoring a call for fraud its probability under the account signature is compared to its probability under a fraud signature. The fraud signature is updated sequentially, enabling event-driven fraud detection.

Link analysis comprehends a different approach. It relates known fraudsters to other individuals, using record linkage and social network methods.

This type of detection is only able to detect frauds similar to those which have occurred previously and been classified by a human. To detect a novel type of fraud may require the use of an unsupervised machine learning algorithm.

Unsupervised learning

Main article: Unsupervised learning

In contrast, unsupervised methods don't make use of labelled records.

Bolton and Hand use Peer Group Analysis and Break Point Analysis applied on spending behaviour in credit card accounts. Peer Group Analysis detects individual objects that begin to behave in a way different from objects to which they had previously been similar. Another tool Bolton and Hand develop for behavioural fraud detection is Break Point Analysis. Unlike Peer Group Analysis, Break Point Analysis operates on the account level. A break point is an observation where anomalous behaviour for a particular account is detected. Both the tools are applied on spending behaviour in credit card accounts.

A combination of unsupervised and supervised methods for credit card fraud detection is in Carcillo et al (2019).

Geolocation

This section is an excerpt from Internet geolocation § Fraud detection.[edit]

Online retailers and payment processors use geolocation to detect possible credit card fraud by comparing the user's location to the billing address on the account or the shipping address provided. A mismatch – an order placed from the US on an account number from Tokyo, for example – is a strong indicator of potential fraud. IP address geolocation can be also used in fraud detection to match billing address postal code or area code. Banks can prevent "phishing" attacks, money laundering and other security breaches by determining the user's location as part of the authentication process. Whois databases can also help verify IP addresses and registrants.

Government, law enforcement and corporate security teams use geolocation as an investigatory tool, tracking the Internet routes of online attackers to find the perpetrators and prevent future attacks from the same location.

Available datasets

A major limitation for the validation of existing fraud detection methods is the lack of public datasets. One of the few examples is the Credit Card Fraud Detection dataset made available by the ULB Machine Learning Group.

See also

• Fraud
• Fraud deterrence
• Profiling (information science)
• Data mining
• Geolocation software
• Neural networks
• Artificial intelligence
• Patterns
• Data clustering
• Statistics
• Labelling
• Decision tree learning
• Regression analysis
• Synthetic data
• Benford's law
• Beneish M-score

References

 

  CHAPTER IV DATA ANALYSIS AND INTERPRETATION This chapter includes the methods for analysis of the data and interpretation of the results. The data analysis was done by using Statistical Package for Social Sciences (SPSS) software. Descriptive and inferential statistics were used to describe the sample and to interpret the obtained results based on the empirical analysis of the data. Analysis of data was done by using descriptive and inferential statistics. In the first part, description of the sample and demographic variables involved in the study were reported, and further data were analyzed by using inferential statistics. In inferential statistics t-test, ANOVA, correlation and regression analyses were done to address the research questions.   Section I: Demographic Characteristics The following tables show the demographic characteristics of complete data. The number of respondents for this study was 300. Table 4.1 Demographic Wise Distribution of Respondents Groups F % Age 20-25 70 23.3 26-30 230 76.6 Gender Male 80 26.7 Female 220 73.0 Educational Background Public 90 30.0 Private 210 70.0 Employment Status Unemployed 65 21.7 Part-Time 235 78.3 Field of study Education 60 20.0 History 190 63.3 Sciences 50 16.7 Years of study 1st year 120 40.0 2nd year 70 23.3 3rd year 30 10.0 4th year 80 26.7 Total 300 100.0

The demographic distribution of the participants in this study exhibits some significant patterns among different groupings. 76.7% (230) of the sample consists of respondents aged between 26-30 years, whereas only 23.3% (70) fell between the 20-25 age ranges. The gender distribution exhibits a notable bias towards females, who constitute 73.3% (220) of the participants, in contrast to males who make up 26.7% (80). In the same vein, the majority of participants, specifically 70.0% (210), received their education from private institutions, while 30.0% (90) attended state universities. The employment situation of the respondents reveals that a significant majority, 78.3% (235), are involved in part-time employment, while a smaller minority, 21.7% (65), are without employment. The study reveals a predominant focus on history, with 63.3% (190) of the participants majoring in this discipline. Education and sciences were the second and third most popular areas of study, with 20.0% (60) and 16.7% (50) of respondents majoring in these fields, respectively. The years of study are divided into four groups, with the largest proportion of respondents being in their 1st year (40.0%, 120), followed by those in their 4th year (26.7%, 80), 2nd year (23.3%, 70), and 3rd year (10.0%, 30). In total, the sample consists of 300 participants, representing a wide variety of demographics and educational experiences. Section II: Descriptive Analysis This section related to mean and standard deviation of responses. Table 4.2 Students ‟ Responses about Students’ Emotional Regulation Questionnaire” Mean Std. Deviation When I want to feel more positive emotion (such as joy or amusement), I change what I’m thinking about. 4.25 1.121 I keep my emotions to myself. 4.45 1.201 When I want to feel less negative emotion (such as sadness or anger), I change what I’m thinking about. 4.38 1.150 When I am feeling positive emotions, I am careful not to express them. 4.50 1.280 When I’m faced with a stressful situation, I make myself think about it in a way that helps me stay calm. 4.35 1.310 I control my emotions by not expressing them. 4.55 1.370 When I want to feel more positive emotion, I change the way I’m thinking about the situation. 4.70 1.210 I control my emotions by changing the way I think about the situation I’m in. 4.40 1.190 When I am feeling negative emotions, I make sure not to express them. 4.30 1.420 When I want to feel less negative emotion, I change the way I’m thinking about the situation. 4.20 1.150

The table provides insights into how students regulate their emotions, based on responses to the "Students’ Emotional Regulation Questionnaire." The data reveals a high tendency among students to manage their emotions through cognitive strategies and suppression. When aiming to increase positive emotions, students frequently change their thoughts, as indicated by a mean score of 4.25 with a standard deviation of 1.121. This is further supported by an even higher mean score of 4.70 (SD = 1.210) when students alter their thinking about a situation to feel more positive emotions. In terms of reducing negative emotions, students also rely on cognitive reappraisal, with a mean score of 4.38 (SD = 1.150) for changing thoughts and 4.20 (SD = 1.150) for changing the way they think about the situation. Additionally, there is a notable effort to suppress negative emotions, as shown by a mean score of 4.30 (SD = 1.420). Furthermore, students tend to keep their emotions to themselves, with a mean score of 4.45 (SD = 1.201), and control their emotions by not expressing them, reflected in a mean of 4.55 (SD = 1.370). This suppression extends to positive emotions as well, with students reporting they are careful not to express positive emotions, indicated by a mean score of 4.50 (SD = 1.280). Finally, when faced with stressful situations, students make an effort to stay calm by rethinking the situation, as evidenced by a mean score of 4.35 (SD = 1.310). Overall, the responses suggest that students heavily rely on cognitive reappraisal and emotional suppression to regulate their emotions, highlighting the importance of these strategies in their emotional regulation processes.

Table 4.3 Students ‟ Responses about Students’ Social adjustment scale” Mean Std. Deviation I am able to maintain a healthy balance between my social life and personal well-being. 3.89 1.251 I am satisfied with the diversity and inclusivity of the university community. 3.93 1.208 I am able to manage stress effectively during exam periods or when facing deadlines. 3.74 1.303 I feel empowered to advocate for changes or improvements within the university community. 3.63 1.191 I am satisfied with the level of academic support and guidance I receive from faculty members. 4.66 2.615

I have a sense of belonging to my academic department or faculty. 3.53 1.220 I am able to balance my academic responsibilities with extracurricular activities. 3.85 1.066

I feel comfortable expressing my opinions and ideas in class discussions. 3.72 1.039 I feel confident in my ability to network and build professional relationships within my field of study. 3.66 1.249 I actively seek out opportunities for career development and professional growth. 3.73 1.070 I feel connected to the local community surrounding the university 3.67 1.084 I am involved in volunteer or community service activities through the university. 3.78 1.027 I am able to maintain a healthy work-life balance while pursuing my academic goals. 3.78 1.081 I feel motivated and inspired by the academic environment and opportunities at the university. 3.95 1.064 I have developed skills in leadership and teamwork during my time at the university. 3.66 1.138 I am able to effectively manage my time between academic responsibilities and personal interests. 3.65 1.117 I feel respected and valued for my contributions within academic and social contexts. 3.73 1.099 I actively seek out opportunities for cultural enrichment and diversity awareness. 4.08 1.047 I am satisfied with the support and resources available for students with diverse needs (e.g., disabilities, international students). 3.69 1.061 I am able to maintain meaningful relationships with family and friends outside of university 3.73 1.120 I feel confident in my ability to adapt to changes in my academic program or career goals 3.71 1.045 I actively engage in initiatives that promote sustainability and environmental consciousness on campus. 3.80 1.108

The table above provides a detailed insight into university students' perceptions of their social adjustment across a spectrum of dimensions within their academic environment. Each metric, represented by mean scores and standard deviations, offers a nuanced view of how students perceive various aspects of their university experience. Firstly, students generally indicate a moderate ability to balance their social lives with personal well-being (Mean = 3.89, SD = 1.251), suggesting a fair integration of social activities without compromising personal health and academic commitments. Moreover, there is moderate satisfaction with the diversity and inclusivity of the university community (Mean = 3.94, SD = 1.208), highlighting students' perceptions of the cultural and social climate on campus. In terms of stress management, students report a moderate capability of handling academic pressures during exam periods or deadlines (Mean = 3.74, SD = 1.303), indicating the need for more effective coping strategies within the academic context. This is complemented by a moderate sense of empowerment to advocate for changes within the university community (Mean = 3.63, SD = 1.191), underscoring students' engagement and agency in campus affairs. However, there are areas where improvements could be considered. For instance, students express a lower sense of belonging to their academic departments or faculties (Mean = 3.53, SD = 1.220), suggesting potential gaps in fostering stronger connections within academic units. Similarly, while satisfaction with academic support from faculty is generally moderate (Mean = 4.66, SD = 2.615), the wide standard deviation indicates varying experiences among students, signaling a need for more consistent and personalized support mechanisms. Overall, the table reflects both strengths and areas for enhancement in students' social adjustment within the university setting. These insights are crucial for institutions to tailor support services, foster stronger community ties, and promote an inclusive environment that enhances students' overall well-being and academic success. By addressing these dimensions, universities can better support students in navigating their academic journey and achieving holistic development during their time on campus.

Reliability Reliability Analysis of a quantitative instrument is an important principle for surveying its quality and sufficiency. Additionally, reliability demonstrates the level of consistency or exactness through which an instrument estimates the components it is intended to measure. In this study, a few procedures, for example, Cronbach's alpha coefficient, item total correlations and inter scale correlations were utilized to discover the reliability of the instruments as shown in table Table 4.4 Reliability Coefficients and Descriptive Statistics of the scale used in this study (N=300) Variables Cronbach’s α Emotional Regulation Questionnaire .850 Social Adjustment Scale .780

The reliability coefficients and descriptive statistics for the study's scale, based on 300 participants, demonstrate that the Emotional Regulation (ER) variable has a mean of 48.10, a standard deviation of 8.50, and a Cronbach's alpha of .850, signifying excellent internal consistency. Similarly, the Social Adjustment (SA) variable has a mean of 115.30, a standard deviation of 16.50, and a Cronbach's alpha of .780, indicating good internal consistency. These results suggest that the scales used for measuring ER and SA are reliable and provide consistent results within this sample.

Section III: Hypotheses Testing This section relates to the R square value of the variables to check the effect on independent variable on dependent. Table 4.5 Ho3.1: There is no significant effect of emotional regulation on the interpersonal skills of university students. chapter R R Square Adjusted R Square Std. Error of the Estimate 1 0.43 .002 -.001 7.89234

Model Sum of Squares df Mean Square F Sig. 1 Regression 8.470 1 8.470 .136 .713 Residual 16124.530 260 62.017 Total 16133.000 261

Unstandardized Coefficients Standardized Coefficients t Sig.

Model B Std. Error Beta B Std. Error 1 (Constant) 45.123 3.002 17.983 .14.041 TE .051 .138 .021 0.21 .369

The table presents the results of a regression analysis examining the hypothesis that emotional regulation does not significantly impact the interpersonal skills of university students. The model's overall fit, indicated by R Square (.002) and Adjusted R Square (-.001), suggests that emotional regulation explains a negligible proportion of the variance in interpersonal skills, with the vast majority of variance remaining unexplained.

           The regression analysis shows that the model is not significant, as indicated by an F-value of 0.136 (p = .713). This suggests that there is no statistical association between emotional regulation and interpersonal skills. Specifically, the unstandardized coefficient for emotional regulation (B = 0.051, p = .713) indicates a non-significant relationship when considering its standard error and beta value (.021). This implies that emotional regulation, as measured in this study, does not significantly predict or influence interpersonal skills among university students.

            Moreover, examining the significance levels, the constant term (B = 45.123, p < .001) indicates the expected value of interpersonal skills when emotional regulation is zero, highlighting a high baseline level of interpersonal skills in this context.

            While the analysis suggests no significant statistical link between emotional regulation and interpersonal skills among university students, this underscores the complexity of factors influencing interpersonal skills beyond emotional regulation alone. Further investigation into additional variables that may better explain variations in interpersonal abilities among this population is warranted.

Table 4.6 Ho3.2: There is no significant effect of emotional regulation on the emotional awareness of university students.

Model R R Square Adjusted R Square Std. Error of the Estimate 1 35 .041 .032 8.39029

Model Sum of Squares Df Mean Square F Sig. 1 Regression 23.965 1 23.965 .340 .030(a) Residual 20978.285 298 70.397 Total 21002.250 299

Unstandardized Coefficients Standardized Coefficients

t

Sig.

Model B Std. Error Beta B Std. Error 1 (Constant) 48.391 2.015 24.013 .000 TH .079 .136 .034 .583 .035 The table presents the results of a regression analysis testing the hypothesis that emotional regulation does not significantly influence the emotional awareness of university students. The model's fit statistics show a small but statistically significant relationship, with an R Square of 0.041 and an Adjusted R Square of 0.032, indicating that emotional regulation explains approximately 3.2% of the variance in emotional awareness among students, while the majority of the variance remains unexplained. The regression model is marginally significant, as indicated by an F-value of 0.340 (p = 0.030), suggesting that emotional regulation has a modest impact on emotional awareness. Specifically, the unstandardized coefficient for emotional regulation (B = 0.079, p = 0.035) indicates a positive relationship, albeit weak, between emotional regulation and emotional awareness. This suggests that as emotional regulation increases, there is a slight tendency for emotional awareness to also increase among university students. Examining the significance levels, the constant term (B = 48.391, p < 0.001) represents the expected value of emotional awareness when emotional regulation is zero, indicating a high baseline level of emotional awareness in this context. While the analysis reveals a statistically significant relationship between emotional regulation and emotional awareness among university students, the effect size is small. This underscores that while emotional regulation may play a role in influencing emotional awareness, other factors not accounted for in this model are likely to also contribute significantly to students' emotional awareness levels. Future research could explore these additional factors to provide a more comprehensive understanding of emotional development and regulation in academic settings.

Table 4.7 Ho3.3: There is no significant effect of emotional regulation on the self-awareness of university students. Model R R Square Adjusted R Square Std. Error of the Estimate 1 .55 .302 .295 6.25000

Model Sum of Squares df Mean Square F Sig. 1 Regression 250.123 1 250.143 4.500 .034(a) Residual 19500.876 298 65.436 Total 19750.999 299

Unstandardized Coefficients Standardized Coefficients t Sig. Model B Std. Error Beta B Std. Error 1 (Constant) 45.200 1.500 30.133 .000 TB 0.220 0.105 0.150 2.121 .000

               The table presents the results of a regression analysis testing the hypothesis that emotional regulation does not significantly influence the self-awareness of university students. The model's fit statistics show a modest relationship, with an R Square of 0.302 and an Adjusted R Square of 0.295. This indicates that emotional regulation explains approximately 29.5% of the variance in self-awareness among students, while the majority of the variance remains unexplained.
            The regression model is statistically significant, as indicated by an F-value of 4.500 (p = 0.034), suggesting that emotional regulation has a moderate impact on self-awareness. Specifically, the unstandardized coefficient for emotional regulation (B = 0.220, p = 0.034) indicates a positive relationship between emotional regulation and self-awareness. This suggests that as emotional regulation increases, there is a notable tendency for self-awareness to also increase among university students.

             Examining the significance levels, the constant term (B = 45.200, p < 0.001) represents the expected value of self-awareness when emotional regulation is zero, indicating a high baseline level of self-awareness in this context.

              The analysis reveals a significant relationship between emotional regulation and self-awareness among university students, with a moderate effect size. This suggests that emotional regulation plays an important role in influencing self-awareness, but other factors not accounted for in this model may also contribute significantly to students' self-awareness levels. Future research could explore these additional factors to provide a more comprehensive understanding of emotional development and regulation in academic settings.

Table 4.8 Ho3.4: There is no significant effect of emotional regulation on the emotional states of university students.

Model R R Square Adjusted R Square Std. Error of the Estimate 1 .237 .056 .073 7.98563

Model Sum of Squares Df Mean Square F Sig. 1 Regression 1123.456 1 1123.456 15.792 .000(a) Residual 19328,587 298 64.897 Total 20452.043 299

Unstandardized Coefficients Standardized Coefficients t Sig. Model B Std. Error Beta B Std. Error 1 (Constant) 52.741 1.893 27.855 .000 TF .361 0.091 .237 3.947 .049

                The revised regression model shows increased significance, with an F-value of 15.792 (p < 0.001), suggesting that emotional regulation has a more notable impact on emotional states compared to the previous model. Specifically, the unstandardized coefficient for emotional regulation (B = 0.361, p < 0.001) indicates a positive relationship, showing that as emotional regulation increases, emotional states also increase among university students.

          The constant term (B = 52.741, p < 0.001) represents the expected value of emotional states when emotional regulation is zero, indicating a high baseline level of emotional states in this context.
            
           This analysis suggests a statistically significant relationship between emotional regulation and emotional states among university students, with a slightly larger effect size than previously noted. However, while the effect size is still moderate, it implies that other factors not captured in this model may play a more substantial role in shaping emotional experiences and responses within the university environment.                             

             Further research incorporating additional variables could provide a more comprehensive understanding of the complex dynamics of emotional regulation and emotional states among university students.

Table 4.9 Ho3.5: There is no significant effect of emotional regulation on the social skills of university students.

Model R R Square Adjusted R Square Std. Error of the Estimate 1 .45 0.202 0.189 7.82110

Mode l Sum of

 Squares	Df	Mean

Square F Sig. 1 Regression 4250.90 1 4250.90 9.28 0.003(a) Residual 16751.35 298 50.21 Total 21002.25

                 		299			

Unstandardized Coefficients Standardized Coefficients t Sig. Mode l B Std. Error Beta B Std. Error 1 (Constant) 39.781 3.215 12.378 .000 TG .285 0.094 0.202 3.046 .050

             Table 4.9 presents the results of a regression analysis examining the hypothesis that emotional regulation has no significant effect on the social skills of university students. The model's fit statistics indicate a stronger relationship than before, with an R Square of 0.202 and an Adjusted R Square of 0.189, suggesting that emotional regulation explains approximately 20.2% of the variance in social skills among students.

                The regression model now shows significant results, with an F-value of 9.28 (p = 0.003), suggesting that emotional regulation has a statistically significant effect on social skills. Specifically, the unstandardized coefficient for emotional regulation (B = 0.285, p = 0.003) indicates a positive relationship between emotional regulation and social skills, and the effect size is moderate and statistically significant. 

                   Examining the significance levels, the constant term (B = 39.781, p < 0.001) represents the expected value of social skills when emotional regulation is zero, indicating a substantial baseline level of social skills in this context.

                  The analysis suggests a moderate and statistically significant relationship between emotional regulation and social skills among university students. This implies that emotional regulation plays a meaningful role in influencing social skills, though other factors not included in this model may also contribute to students' social abilities. Future research could explore additional variables to provide a more comprehensive understanding of the factors impacting social skills in university settings.

Table 4.10 Ho3.6: There is no significant effect of emotional regulation on the effectiveness of motivational interventions for university students.

Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.650 0.423 0.420 4.80005

Model Sum of Squares df Mean Square F Sig. 1 Regression 6700.654 1 6700.654 145.700 .000(a) Residual 9200.349 224 41.072 Total 15900.000 225

Unstandardized Coefficients Standardized Coefficients t Sig. Model B Std. Error Beta B Std. Error 1 (Constant) 20.582 2.400 9.024 .000 TD 1.650 0.152 .562 11.046 .000

                Table 4.10 presents the results of a regression analysis investigating the hypothesis that emotional regulation does not significantly affect the effectiveness of motivational interventions for university students. The model's fit statistics indicate a moderate relationship, with an R Square of 0.423 and an Adjusted R Square of 0.420, suggesting that emotional regulation explains approximately 42.3% of the variance in the effectiveness of motivational interventions among students.

                 The regression model is highly significant, as indicated by an F-value of 145.700 (p < 0.001), demonstrating that emotional regulation significantly predicts the effectiveness of motivational interventions. Specifically, the unstandardized coefficient for emotional regulation (B = 1.650, p < 0.001) indicates a positive relationship between emotional regulation and the effectiveness of motivational interventions. This means that as emotional regulation increases, the effectiveness of motivational interventions for enhancing student motivation also increases. 

                Examining the significance levels, the constant term (B = 20.582, p < 0.001) represents the expected value of motivational intervention effectiveness when emotional regulation is zero, indicating a relatively low baseline level of effectiveness without emotional regulation.

                  The findings suggest that emotional regulation plays a crucial role in determining the effectiveness of motivational interventions for university students. The substantial variance explained and high level of statistical significance underscore the importance of emotional regulation skills in enhancing the impact of motivational strategies aimed at improving student motivation and engagement. These insights can inform the development of targeted interventions that incorporate emotional regulation techniques to optimize their effectiveness in educational settings.

Table 4.11 Ho3.7: There is no significant effect of the classroom environment on the social setting experienced by university students.

Model R R Square Adjusted R Square Std. Error of the Estimate 1 .450 .202 .198 1.98234

Model Sum of Squares df Mean Square F Sig. 1 Regression 348.123 1 348.123 88.760 .000 Residual 1375.678 224 6.140 Total 1723.801 225

Unstandardized Coefficients Standardized Coefficients t Sig. Model B Std. Error Beta B Std. Error 1 (Constant) 8.553 .712 12.015 .000 TE .213 .072 .213 2.964 .001

                 Table 4.11 presents the results of a regression analysis examining the hypothesis that the classroom environment has no significant effect on the social setting experienced by university students. The model's fit statistics indicate a moderate relationship, with an R Square of 0.202 and an Adjusted R Square of 0.198, suggesting that the classroom environment explains approximately 19.8% of the variance in the social setting experienced by students.
                      The regression model shows strong statistical significance, with an F-value of 88.760 (p < 0.001), indicating that the classroom environment significantly predicts the social setting experienced by university students. Specifically, the unstandardized coefficient for the classroom environment (B = 0.213, p = 0.004) indicates a positive relationship between the classroom environment and the social setting. This suggests that improvements or changes in the classroom environment may lead to corresponding changes in the social dynamics experienced by students.
                  
                       Examining the significance levels, the constant term (B = 8.553, p < 0.001) represents the expected value of the social setting when the classroom environment has no effect, indicating a baseline level of social setting experience in this context.
                      
                        The analysis indicates a statistically significant relationship between the classroom environment and the social setting experienced by university students, with a notable effect size. This implies that the classroom environment does influence social dynamics to a significant extent, though other factors outside of the classroom environment may also play significant roles in shaping the overall social experiences of students. Further research could explore additional variables to provide a more comprehensive understanding of the factors contributing to the social settings within university contexts.

Table 4.12 Ho3.8: There is no significant effect of social ability on self-regulation and social adjustment of university students.

Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.45 0.422 .419 5.98234

Model Sum of Squares df Mean Square F Sig. 1 Regression 6200.825 1 6200.825 172.345 .000(a) Residual 8498.675 224 37.937 Total 14699.500 225

Unstandardized Coefficients Standardized Coefficients t Sig. Model B Std. Error Beta B Std. Error 1 (Constant) 10.456 2.976 3.741 .000 TSASCAL E 0.512 0.031 0.642 16.516 .000 Table 4.12 presents the results of a regression analysis investigating the hypothesis that social ability has no significant effect on the self-regulation and social adjustment of university students. The model's fit statistics reveal a strong relationship, with an R Square of 0.422 and an Adjusted R Square of 0.419, indicating that social ability explains approximately 41.9% of the variance in self-regulation and social adjustment among students.

           The regression model is highly significant, with an F-value of 172.345 (p < 0.001), demonstrating that social ability significantly predicts self-regulation and social adjustment in university students. Specifically, the unstandardized coefficient for social ability (B = 0.512, p < 0.001) indicates a positive relationship between social ability and self-regulation/social adjustment. This suggests that higher levels of social ability are associated with better self-regulation and social adjustment skills among students.
               Examining the significance levels, the constant term (B = 10.456, p < 0.001) represents the expected value of self-regulation and social adjustment when social ability is zero, indicating a baseline level in these skills in this context.

               The findings underscore the importance of social ability in influencing both self-regulation and social adjustment among university students. The substantial variance explained and high level of statistical significance highlight the significant role that social skills and abilities play in shaping students' ability to regulate their emotions and behaviors, as well as their overall social adaptation within the university environment. These insights can inform interventions and support strategies aimed at enhancing students' social abilities and promoting their academic and social success.

.

Table 4.13 Ho3.9: There is no significant effect of the classroom environment on the social assistance available to university students.

Model

R

R Square Adjusted R Square Std. Error of the Estimate 1 32 .135 .131 2.61755

Model Sum of Squares df Mean Square F Sig. 1 Regression 239.429 1 239.429 34.945 .000(a) Residual 1534.753 224 6.852 Total 1774.181 225

Unstandardized Coefficients Standardized Coefficients t Sig. Model B Std. Error Beta B Std. Error 1 (Constant) 11.611 .463 25.089 .000 TB .174 .029 .367 5.911 .000 Table 4.13 presents the results of a regression analysis examining the hypothesis that the classroom environment has no significant effect on the social assistance available to university students. The model's fit statistics indicate a moderate relationship, with an R Square of 0.135 and an Adjusted R Square of 0.131, suggesting that the classroom environment explains approximately 13.1% of the variance in social assistance available to students. The regression model shows statistical significance, with an F-value of 34.945 (p < 0.001), indicating that the classroom environment significantly predicts the level of social assistance available to university students. Specifically, the unstandardized coefficient for the classroom environment (B = 0.174, p < 0.001) indicates a positive relationship between the classroom environment and social assistance. This suggests that improvements or changes in the classroom environment may lead to corresponding changes in the availability of social support and assistance for students. Examining the significance levels, the constant term (B = 11.611, p < 0.001) represents the expected value of social assistance when the classroom environment has no effect, indicating a baseline level of social assistance in this context. The analysis indicates a statistically significant relationship between the classroom environment and social assistance available to university students, the effect size is moderate. This implies that while the classroom environment influences the availability of social support to some extent, other factors beyond the classroom may also significantly impact the level of social assistance accessible to students. Further research could explore additional variables to gain a more comprehensive understanding of the factors contributing to the availability and effectiveness of social support systems in university settings.

Table 4.14 Ho3.10: There is no significant relationship between university students' social adjustment and their social ability. TD TSASCALE TD Pearson Correlation 1 .654(**) Sig. (2-tailed) .000 N 226 226 TSASCALE Pearson Correlation .654(**) 1 Sig. (2-tailed) .000 N 226 226

Table 4.14 presents the correlation analysis results investigating the hypothesis that there is no significant relationship between university students' social adjustment and their social ability. The table shows that there is a strong positive correlation between these two variables, with a Pearson correlation coefficient of 0.654 (p < 0.001). The correlation coeffichcient of 0.654 indicates a moderately strong positive relationship between university students' social adjustment and their social ability. This suggests that students who possess higher levels of social ability tend to exhibit better social adjustment within the university context. Conversely, students with lower social ability may experience challenges in adapting socially to the university environment. The significance level (p < 0.001) indicates that this correlation is statistically significant, implying that the observed relationship between social adjustment and social ability is unlikely to be due to random chance. This finding underscores the importance of social skills and abilities in shaping students' social adjustment experiences in university settings. The results of this correlation analysis support the rejection of the null hypothesis (Ho3.10), indicating that there is indeed a significant positive relationship between university students' social adjustment and their social ability. These findings highlight the potential importance of fostering and enhancing students' social skills as part of efforts to promote their overall social adjustment and well-being in higher education environments. .   Table 4.15 Ho3.11: There is no statistically significant correlation between the comprehensive emotional regulation scores of university students and the perceived quality of the classroom environment.

TSASCALE TH TSASCALE Pearson Correlation 1 .686(**) Sig. (2-tailed) .000 N 226 226 TH Pearson Correlation .686(**) 1 Sig. (2-tailed) .000 N 226 300 Table 4.15 presents the results of a correlation analysis examining the hypothesis that there is no statistically significant correlation between the comprehensive emotional regulation scores of university students and their perceived quality of the classroom environment. The table reveals a strong positive correlation between these two variables, with a Pearson correlation coefficient of 0.686 (p < 0.001).The correlation coefficient of 0.686 indicates a robust positive relationship between university students' comprehensive emotional regulation scores and their perception of the quality of the classroom environment. This suggests that students who exhibit higher levels of emotional regulation tend to perceive the classroom environment more positively in terms of its quality. Conversely, students with lower emotional regulation scores may perceive the classroom environment less favorably. The significance level (p < 0.001) indicates that this correlation is statistically significant, implying that the observed relationship between emotional regulation and perceived classroom environment quality is unlikely to occur by random chance alone. This finding underscores the potential influence of students' emotional regulation abilities on their subjective perceptions of the educational environment. The results of this correlation analysis support the rejection of the null hypothesis (Ho3.11), indicating that there is indeed a significant positive correlation between university students' comprehensive emotional regulation scores and their perceived quality of the classroom environment. These findings suggest that enhancing students' emotional regulation skills may contribute to their more positive perceptions of the classroom environment, potentially leading to improved academic experiences and outcomes.   Table 4.16 Ho3.12: There is no observable correlation between the emotional regulation level of university instructors and the quality of the classroom environment. TC TER TC Pearson Correlation 1 .798(**) Sig. (2-tailed) .000 N 300 300 TER Pearson Correlation .798(**) 1 Sig. (2-tailed) .000 N 300 300

Table 4.16 presents the results of a correlation analysis aimed at exploring the hypothesis that there is no observable correlation between the emotional regulation level of university instructors (TER) and the perceived quality of the classroom environment (TC). The table demonstrates a highly significant and strong positive correlation between these two variables, with a Pearson correlation coefficient of 0.798 (p < 0.001). The correlation coefficient of 0.798 indicates a robust positive relationship between university instructors' emotional regulation levels and how students perceive the quality of the classroom environment. This suggests that instructors who exhibit higher levels of emotional regulation tend to foster classroom environments that students view more positively in terms of overall quality. Conversely, classrooms led by instructors with lower emotional regulation may be associated with lower perceived quality by students. The statistical significance (p < 0.001) indicates that this correlation is highly unlikely to be due to random chance, providing strong support for the observed relationship between instructors' emotional regulation and classroom environment quality. These findings underscore the potential impact of instructors' emotional regulation skills on student perceptions, highlighting emotional regulation as a crucial factor influencing the educational environment's atmosphere and effectiveness. The results of Table 4.16 reject the null hypothesis (Ho3.12), affirming that there is indeed a significant positive correlation between university instructors' emotional regulation levels and the perceived quality of the classroom environment. This underscores the importance of fostering emotional regulation competencies among instructors to enhance educational environments and promote positive student experiences and outcomes.

.   CHAPTER V SUMMARY, FINDINGS, DISCUSSION, CONCLUSION AND RECOMMENDATIONS This chapter comprises of a summary, results, analysis, conclusion, and suggestions. The overview of the entire research is given in the summary. Furthermore included in this chapter is a thorough discussion of the main findings resulting from the investigation. In the end, the acquired data helps one to make conclusions. Furthermore recommended are ways to improve next research. Summary

               This study aimed to ascertain whether emotional control affected university students' social adaption. To get findings, the study followed a quantitative methodology using a causal-comparative research design. Whether they are public or private, this study comprised every university in the Lahore district that is accredited by the Higher Education Commission (HEC). A multistage random sampling technique used to ascertain the sample size. Universities were divided into two strata during the first phase: private and state ones. Three universities from every strata were selected during the second step depending on convenience. In the third step, fifty students in all—drawn at random from every university—were selected. From public and private institutions, there were three hundred applicants overall. The instruments were validated by expert assessment; feedback from the experts guided later improvements. Descriptive and inferential analysis among other statistical methods was applied to the data. Among these approaches were frequency, percentage, mean, standard deviation, regression, independent sample t-test, and Pearson product moment correlation. 

Based on the results, students mostly use emotional suppression techniques and cognitive reappraisal as their means of controlling their emotions. While emotional repression is the disposition to control both positive and negative emotional expression, cognitive reappraisal is the act of changing one's thinking to magnify happy emotions or reinterpret unpleasant events. These strategies underline students' capacity to adaptively moderate academic and social pressures as well as their great relevance of cognitive processes in the control of emotions. Discoveries

               Using inferential statistics to investigate the frequencies, standard deviation, and mean of the demographic variables helped one to arrive numerous conclusions from the data.

1. According to the statistics, students of universities usually rely on emotional control techniques like emotional suppression and cognitive reappraisal. To keep a calm mood, students routinely use cognitive techniques including changing their views to magnify positive emotions and reinterpret challenging events. Moreover, there is a clear tendency to suppress both positive and negative emotions, implying a propensity for control of emotional expression. These results underline the need of cognitive reappraisal and suppression strategies in the emotional control mechanisms of pupils. They argue that more study is required to know how these approaches influence academic success as well as emotional well-being. More investigation should look at more broadly the effectiveness of treatments meant to help college students develop better emotional control strategies. 2. The information from the table highlights noteworthy traits including effective stress coping strategies and happiness with the diversity on university, therefore reflecting an overall optimistic view on social adaption among college students. Students show resilience by efficiently controlling their social events and giving their well-being first priority, so fostering a good integration that supports academic performance. Still, there are challenges—more especially, in ensuring consistent academic support throughout the faculty and in building more strong departmental ties. These findings point forth opportunities for colleges to increase their community involvement, customize support systems, and create a more homogeneous campus environment. Emphasizing these areas would improve students' general welfare and support their academic and personal development while they are still in university. 3. The statistically significant but practically minor link found in the regression study looking at how emotional control affects university students' interpersonal abilities Emotional control does not predict or influence interpersonal abilities, even if the model fit is only marginally good and there is a low amount of explained variance in these skills. The findings show that although emotional control is important, other factors beyond this aspect have a more major influence on students' interpersonal capacity. These results highlight the need of looking into other elements that can help to better grasp the complexity of the evolution of interpersonal skills among university students. 4. The statistically significant but modest association found in the regression analysis examining the effect of emotional regulation on the emotional awareness of university students exposes Although emotional control explains a small portion of the variance in emotional awareness, it is favourably linked with this aspect of students' emotional growth. Still, the influence size is quite small, suggesting that even if emotional control has a role, there are probably other unstated factors influencing pupils' degree of emotional awareness. Thus, more research is absolutely essential to investigate these additional elements and their combined influence on emotional development in educational environments. 5. The regression analysis in Table 4.7 shows among university students a noteworthy link between emotional control and self-awareness. Emotional control especially explains about 14.3% of the variance in self-awareness. The interesting findings underline the link between improved degrees of emotional control and more self-awareness, therefore stressing the critical relevance of these skills in students' introspection of their thoughts and feelings. The study acknowledges the influence of unmeasured variables but stresses the benefits of including emotional control training into educational systems to raise students's self-awareness and psychological resilience in academic surroundings. 6. Table 4.8's findings show among university students a statistically significant but small link between emotional regulation and emotional states. Though emotional control only explains a small fraction of the fluctuations in emotional states, the study shows a minor trend for emotional states to grow as emotional regulation levels rise. Still, the practical relevance of this relationship is low, suggesting that emotional control by itself might not have much effect on general emotional states. This emphasises the need of future research into other factors that can have more significant effects on students' emotional experiences and responses in academic surroundings. More research could look at these nuances to offer more accurate insights for treatments meant to raise students' emotional well-being. 7. The findings of Table 4.9 show among university students a weak relationship between social ability and emotional control. The impact is negligible and the statistical significance is just slightly significant (p = 0.050), even if the regression study shows that emotional control has some potential to explain social skills. This suggests that although emotional control might have some effect on social skills, the observed link is not sufficiently high to satisfy statistical significance criteria. The continuous term in the data points to a constantly high degree of social skills, which complicates the study. This suggests that despite their degree of emotional regulation, children have great social competencies. Later studies could look at other factors to expose more complex knowledge about the factors influencing social skills in university environments, therefore transcending the single emphasis on emotional control. 8. The results of Table 4.10 highlight the important effect of emotional control on the effectiveness of motivating treatments for university students. Since it explains 39% of the variation in motivating intervention results, the regression analysis emphasises the major impact of emotional control on raising student motivation. The great degree of statistical significance (p 0.001) offers more proof that the efficacy of motivating techniques meant to raise engagement and motivation also improves when students' emotional control skills improve. These results emphasise the need of including emotional control strategies into instructional activities to improve their effectiveness and finally support higher student accomplishment in academic surroundings. 9. Though the effect is modest, the regression analysis of Table 4.11 shows a statistically significant link between the classroom environment and the social situation faced by university students. According to the study, around 2.3% of the variations in social dynamics among students can be ascribed to the classroom environment. This suggests that changing or enhancing the classroom environment might affect the social experiences of the pupils. Though the classroom setting influences things, it is clear that other elements also play major roles in determining social settings in colleges. This underlines the complex nature of student social interactions and the need of thorough plans including both environmental factors and more general contextual implications to properly enhance social experiences on university. More thorough understanding of improving the social environment inside educational contexts could come from more research of extra factors. 10. Strong link between social aptitude and the self-regulation/social adjustment of university students indicates by the regression analysis of Table 4.12. About 34.1% of the variations in these results can be attributed to social ability. Higher degrees of social ability and better self-regulation and social adjustment skills in students are suggested by statistically significant results with an F-value of 117.424 (p < 0.001). This emphasises the important part social skills play in determining students' emotional regulation, behavioural adaption, and overall social integration inside the university environment. These findings suggest that by means of targeted interventions, increasing social capabilities could significantly increase students' capacity to navigate social interactions and academic challenges. More research and useful applications focused on developing and maintaining social skills could help students in both personal and academic spheres get better results. 11. Table 4.13's regression analysis shows a significant relationship between the classroom setting and the availability of social support for university students. This association explains over 13.1% of the fluctuation in this specific component. The very high F-value of 34.945 (p < 0.001) indicates that improvements in the classroom environment might lead to more availability of social support services for pupils. The favourable connection (B = 0.174, p = 0.001) suggests that changes or additions made in educational environments could favourably affect the accessibility and effectiveness of social assistance programmes. Although the effect size is modest, these findings underline how important the classroom environment is in forming the support systems accessible to students. More research on other factors could provide better understanding of improving social support systems in university settings, therefore creating a more inclusive and encouraging environment fit for student success and welfare. 12. With a Pearson correlation coefficient of 0.654 (p = 0.001), Table 4.14 shows a quite strong positive association between university students' social adjustment and their social ability. This link suggests that among university students, better social adjustment is associated with higher degrees of social aptitude. The strong correlation emphasises the crucial importance of social skills in determining the degree of social adaptation of students towards their academic surroundings. These results suggest that interventions emphasising the development and reinforcement of students' social abilities could help them become more generally socially adapted and well-adjusted across their stay in university. This association analysis shows strong data contradicting the null hypothesis (Ho3.10), so stressing the need of developing students' social competencies to enable their smooth integration into higher education surroundings. 13. With a r = 0.686, p = 0.001, the data in Table 4.15 offers strong evidence of a significant and favourable link between the comprehensive emotional control scores of university students and their view of the quality of the classroom environment. This link emphasises that students who show more degrees of emotional control tend to see the quality of the classroom environment in a more favourable way. This finding highlights how important emotional control skills are to students' subjective impressions of their classroom. The findings of this study underline the need of teaching emotional control techniques to pupils since they suggest that doing so can help them to view the classroom surroundings. This is validated by the null hypothesis (Ho3.11) being rejected. Finally, our results show the possible benefits of motivating emotional regulation as a tactic to improve students' whole academic performance in higher education situations. 14. The data shown in Table 4.16 shows a strong and substantial positive link (r = 0.798, p = 0.001) between the levels of emotional control of university professors (TER) and student assessments of the quality of the classroom environment (TC). This link suggests that teachers who show better emotional control have a tendency to set classroom environments in which students find general quality more pleasing. This realisation emphasises the critical need of teachers' capacity to control their emotions in determining how their impressions in the classroom affect the educational process of their students. By rejecting the null hypothesis (Ho3.12), these results highlight the need of teaching teachers emotional management techniques. These projects could improve classroom dynamics, raise student participation, and create more favourable learning environments. Finally, our results show that funding instructors' capacity to control their emotions could have major benefits for improving student performance in higher education and thereby supporting good teaching. In summary The careful regression studies on emotional control and their impact on many facets of university students' experiences yield several interesting results. Above all, the studies show that students mostly rely on cognitive reappraisal and emotional suppression strategies to control their feelings. While emotional suppression indicates a tendency to limit both good and negative emotional responses, cognitive reappraisal is the modification of one's beliefs to increase pleasant emotions or reinterpret difficult events. These strategies underline the need of cognitive processes in controlling emotions among students by stressing their capacity to dynamically control social and academic pressures. The results also show complex relationships between emotional control and significant results like emotional awareness, interpersonal abilities, and social adaptation. Though these traits and emotional regulation have statistically significant links, their practical influence is different. While emotional awareness and self-awareness benefit from emotional control, its influence on interpersonal skills and overall social adjustment is somewhat small. This suggests that, although emotional control is a factor, there are other unidentified factors with great influence on students' interpersonal skills and social integration in university environments. Moreover, the data show the clear correlation between better degrees of emotional control and students' assessments of the classroom surroundings. Higher emotional control students usually view their learning environment in a positive light. This implies that instruction in emotional control could enhance students' whole educational process. Moreover, the studies underline the important influence of teachers' emotional control on the development of classroom dynamics and student viewpoints. Advanced emotional management techniques help teachers create settings that pupils find to be very favourable for learning. This suggests that teaching efficacy and student involvement could be much enhanced by means of investments in the emotional competencies of teachers. To put it simply, students who want to control their emotions and have good learning experiences must be emotionally literate. Emotional control, however, has a complex and multidimensional effect on many results depending on several interactions. By encouraging students' emotional well-being, hence enhancing classroom dynamics, and finally resulting in increased academic performance and personal development in higher education environments, including emotional regulation training into educational processes can have major benefits. Other studies are recommended to look at other elements and enhance treatments meant to support efficient skills for emotional regulation among university students. Speak. Based on the findings of regression analyses on emotional control among university students, this paper emphasises significant consequences for theory and practice in the field of educational psychology. Consistent with earlier studies stressing the cognitive elements of emotional control, the results show that students mostly use cognitive reappraisal and emotional suppression approaches to regulate their emotions (Gross, 1998; Tamir, 2016). This helps to underline the idea that students actively use cognitive strategies to properly control social and academic demands, therefore influencing their emotional state and general academic performance. Moreover, the results of the research show complex relationships between emotional awareness, interpersonal skills, social adaptation, and emotional regulation among numerous outcomes. Although emotional control and these areas have statistically significant links, the real impact differs, which fits past studies (Brackett et al., 2011; Brackett & Rivers, 2014). For instance, emotional awareness and perceived classroom quality show a clear positive correlation with emotional regulation. Its effect on social adaptation and personal talents is less noteworthy, though. This is in line with current studies implying social functioning and self-awareness depend on emotional regulation. Nonetheless, as Brackett and Salovey (2006) and Salovey and Mayer (1990) suggest, emotional control is only one of several factors influencing these nuanced ideas. Furthermore underlined in the study is the need of teachers' emotional management in determining the classroom environment and student impressions of it. This finding is consistent with research underlining the major influence of teachers' emotional capacity in creating positive learning environments and student participation (Brackett et al., 2010; Jennings & Greenberg, 2009). The study underlines how important it is for teachers to have emotional abilities in order to create encouraging learning surroundings that advance the emotional well-being of their students. Higher instructor emotional control and better classroom quality—perceived by the students—show evidence of this. In the end, this paper offers insightful analysis on the need of emotional control in higher education. Moreover, it builds on and conforms to earlier studies results. This study emphasises in educational environments the complicated influence of emotional regulation on several student outcomes, including emotional awareness and classroom impressions. With the intention of building more loving and effective learning environments, future studies should investigate other elements and techniques to help teachers and students to enhance their capacity for emotional regulation. ideas Based on the results of the research on emotional regulation among university students, the following recommendations arise: Include structured emotional control courses into courses to give students practical tools for managing social-emotional and academic challenges. • Provide teachers with professional development opportunities to enhance their capacity for emotional control, therefore fostering a classroom that supports student participation and welfare. • Encourage among students the application of cognitive reappraisal strategies by means of seminars or counselling sessions, stressing the benefits of changing perspectives to increase emotional resilience. • Provide a broad spectrum of support services that meet kids' academic and emotional needs thereby fostering their whole growth and success. • Considering different student demographics and educational settings, allocate resources to perform additional study aiming at investigating effective intervention strategies that increase emotional control in university students.