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Year : 2023, Volume : 15, Issue : 2
First page : ( 63) Last page : ( 70)
Print ISSN : 0973-3086. Online ISSN : 2583-7087. Published online : 2023  04.
Article DOI : 10.5958/2583-7087.2023.00010.X

Approaches to Learning Social Work Research: Insights for Social Work Educators

Regha V. Sakthi1, Gunavathy J.S.2,*

1Former Librarian, Madras School of Social Work, Chennai, India

2Associate Professor, Department of Social Work, Madras School of Social Work, Chennai, India

*Corresponding Author, jsgunavathy@mssw.in

Online Published on 04 March, 2024.

This study investigates the learning approaches of first-year full-time social work post-graduate students (n=56) in Chennai, focusing on the subject ‘Social Research and Statistics’. Utilizing the Study Process Scale, the research reveals the prevalence of surface learning approaches and gender differences in learning approaches. Three-fold recommendations – to learner, to educator and to the education system have been put forth. Future research avenues exploring interdisciplinary insights, institutional variances, and adaptable learning environments can further enrich our understanding of approaches to learning in social work education. This research contributes not only to the nuanced understanding of learning approaches in the social work discipline but also serves as a compass for learners, educators and institutions in reflecting on the study process to foster meaningful learning experiences in the dynamic landscape of social work education.

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Keywords

Social work education, Approaches to learning, Study Process Scale, Gender differences.

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Introduction

The integration of theoretical and practical learning is paramount for social work trainees, given the human service nature of the profession, where real-life scenarios and individuals in need are at the forefront (Kinni, 2021; Zegarac & Burgund, 2019). The internalisation of their formation is crucial to yield meaningful outcomes during their professional practice. To comprehend the nuances of this internalisation process, it becomes essential to scrutinise the approaches to learning (Entwistle, 2012; Haggis, 2003; Leung & Kember, 2003, Richardson, 2015) among social work trainees.

Approaches to learning encompass learners’ acquired intentions, motives, and strategies influenced by the learning context and situational demands (Entwistle, 1987). These approaches, namely deep and surface learning, represent distinct study processes employed by learners at different times (Marton & Säljö, 1997). A deep approach seeks understanding, encouraging the use and reuse of information across various contexts, especially pertinent when the study material integrates with one’s future profession (Entwistle, 2009; Postareff et al., 2015). On the other hand, a surface approach involves memorisation for tests and exams, proving useful in the initial stages of subject learning (Lindblom-Ylänne et al., 2019).

In higher education, learning outcomes hinge not only on curriculum elements but also on the diverse approaches to learning (Biggs et al., 2022; Diseth, 2003). These approaches cultivate a range of skills, fostering active participation and a facilitative outlook on learning. Variability in learning approaches arises from factors such as student personality, preferences, prior formation, and the unique dynamics of curriculum transaction (Furnham, 2011). At a broader educational level, measuring students’ approaches to learning holds relevance for systematic academic teaching (Trigwell & Prosser, 1991), individual teaching improvement (Richardson, 1990), and identifying students at risk due to ineffective study strategies (Tait & Entwistle, 1996).

However, disciplinary variations in students’ approach to learning are evident (Parpala et al., 2010). The literature highlights subject-specific studies on learning approaches in various disciplines (Almeida et al., 2016; Dereceli, 2017; Google et al., 2023; Rozgonjuk et al., 2020) but underscores the absence of a dedicated exploration in the social work domain. In the context of social work, research is an indirect yet vital method of practice, emphasising the importance of understanding research processes for budding professionals. Therefore, Social Research and Statistics (SRS) is pivotal in the Master of Social Work (MSW) curriculum, providing foundational knowledge of the social research process and essential statistical tools.

Drawing on the facts above, this study aimed to explore the approaches of MSW students to learning SRS. The two primary research questions were: (i) What approaches do full-time MSW students take to study SRS? (ii) What are the differences in the approaches to learning among respondents by individual characteristics such as age, gender and family type?

To further guide our investigation, null hypotheses were formulated for statistical testing. The ensuing sections will delve into the method, results, and discussion, offering valuable insights into the approaches to learning SRS among full-time social work post-graduate students.

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Method

This quantitative research study employed a descriptive design to investigate the approaches to learning adopted by full-time post-graduate students pursuing social work education in a government-aided college affiliated to the University of Madras. The unit of analysis for this research was the individual respondent, representing students within this educational context.

To maintain consistency and control for potential institution-specific and teacher-specific variations in learning approaches, the researchers selected a specific institution through cluster sampling. A government-aided college, chosen randomly from the five such colleges in Chennai (all autonomous institutions), offering the MSW programme, became the focus. The study selected all second-semester MSW students (n=56) learning the subject SRS at the time of the study in the chosen college.

In measuring the approaches to learning, the questionnaire included demographic variables and used the Revised Two Factor Study Process Questionnaire (R-SPQ-2F) developed by Biggs et al. (2001). R-SPQ-2F which assesses deep and surface approaches was found suitable for evaluating the learning approaches of students, using 20 items. Each component comprised two sub-dimensions – Deep Motive and Deep Strategy for Deep Approach, and Surface Motive and Surface Strategy for Surface Approach. Respondents rated their agreement with statements on a 5-point Likert scale, ranging from 1 denoting ‘Only Rarely True of Me’ to 5 denoting ‘Always True of Me’.

Deep Motive gauged internal motivation or curiosity to learn emanating from the students’ personal commitment to learning, measured by items 1, 5, 9, 13, and 17. Deep Strategy involved processes beyond rote learning, such as searching for analogies and theorising, measured by items 2, 6, 10, 14, and 18. Surface Motive captured internal motivation to learn influenced by external consequences of not learning, assessed through items 3, 7, 11, 15, and 19 whereas, Surface Strategy, encompassing rote learning and focus on important points in the learning content, was measured by items 4, 8, 12, 16, and 20. The sum of Deep Motive and Deep Strategy constituted Deep Approach, while the sum of Surface Motive and Surface Strategy constituted Surface Approach. Higher scores indicate higher levels of the construct being measured.

The questionnaire was pre-tested and reliability was confirmed. Data analysis, descriptive and inferential statistics including Independent Sample t-tests, were done using SPSS (Statistical Package for Social Sciences), Version 19.0. The hypotheses formulated were:

Ho1: There is no significant difference in the approach to learning among different age groups of full-time post-graduate social work students.

Ho2: There is no significant difference in the approach to learning between male and female full-time post-graduate social work students.

Ho3: There is no significant difference in the approach to learning based on the family type of full-time post-graduate social work students.

Despite the robustness of the study design, it is important to acknowledge certain limitations. The findings may not be readily generalisable to other subjects or disciplines due to the study’s focus on a specific subject and the social work discipline. Additionally, the study’s applicability to post-graduates in the distance education mode in Chennai and other cities warrants verification. Lastly, the reliance on self-report measures introduces the possibility of human bias and prejudice in the respondents’ responses.

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Results

Demographic Profile of Respondents

Table 1 presents the demographic characteristics of respondents, providing insights into their age, gender, marital status, and family type.

The majority of respondents are below 23 years, constituting 75.0% of the sample. A smaller proportion, 25.0%, belongs to the age group of 23 years and above. The mean age of the respondents is 21.71 years (standard deviation = 1.5 years). The gender distribution indicates a significant representation of females (75.0%) and male respondents making up the remaining 25.0% of the sample. All respondents are categorized as unmarried, reflecting a homogeneous marital status within the sample. The majority of respondents, 85.7%, come from nuclear families. Joint families account for 10.7% of the sample, while only 3.6% are from extended families.

The sample predominantly comprises young, unmarried individuals, with a notable representation of female respondents; family type is primarily nuclear. This demographic profile lays the foundation for further analysis, enabling a nuanced understanding of how these demographic variables may relate to the respondents’ approaches to learning SRS. Subsequent statistical analyses explore potential differences in learning approaches based on these demographic factors.

Descriptive Statistics of Dimensions of Approach to Learning

Table 2 provides descriptive statistics for various dimensions of the respondents’ approach to learning SRS.

The scores for Deep Motive range from a minimum of 11 to a maximum of 23. The mean score is 16.64, indicating a moderate level of internal motivation or curiosity to learn. The standard deviation is 3.27, suggesting some variability in the responses. The 95% confidence interval ranges from 15.78 to 17.50.

Deep Strategy scores vary between 8 and 21. The mean score is 15.46, reflecting moderate employment of cognitive processes beyond rote learning. The standard deviation is 3.30, indicating variability in deep-level strategic approaches. The 95% confidence interval ranges from 14.60 to 16.32.

The Deep Approach, combining Deep Motive and Deep Strategy, has scores ranging from 20 to 43. The mean score is 32.11, signifying a relatively high level of deep learning strategies. The standard deviation is 5.84, indicating some variability in the overall deep approach. The 95% confidence interval ranges from 30.58 to 33.64.

Scores for Surface Motive range from 5 to 15. The mean score is 8.41, indicating a moderate level of internal motivation influenced by external consequences. The standard deviation is 2.78, suggesting some variability in responses. The 95% confidence interval ranges from 7.68 to 9.14.

Surface Strategy scores vary from 6 to 19. The mean score is 11.29, representing a moderate use of surface-level learning strategies. The standard deviation is 2.90, indicating variability in surface-level strategic approaches. The 95% confidence interval ranges from 10.53 to 12.05.

The Surface Approach, combining Surface Motive and Surface Strategy, has scores ranging from 11 to 31. The mean score is 19.70, signifying a moderate level of overall surface learning strategies. The standard deviation is 5.02, indicating some variability in the overall surface approach. The 95% confidence interval ranges from 18.38 to 21.02.

Respondents demonstrate a moderate inclination toward deep learning approaches, with Deep Approach scores notably higher than Surface Approach scores. The confidence intervals provide a range within which the true population values are likely to fall, enhancing the robustness of the descriptive statistics.

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Results of Hypothesis Testing on Learning Approaches

Age Group and Approach to Learning

Table 3 presents the results of independent sample t-tests examining the differences in approach to learning between respondents aged below 23 years and those aged 23 years and above in learning SRS.

For Deep Motive, the t-test indicated a non-significant difference between respondents aged below 23 years (M = 16.55, SD = 3.26) and those aged 23 years and above (M = 16.93, SD = 3.41), with t(54) = -0.45, p = .66. The t-test for Deep Strategy revealed a non-significant difference between the two age groups (t(54) = -1.08, p = .29) with respondents aged up to 22 years (M = 15.05, SD = 3.39) and those aged 23 years and above (M = 16.71, SD = 2.76) exhibiting similar scores. The t-test for Surface Motive indicated a non-significant difference between the age groups (t(54) = -1.78, p = .08). Respondents aged up to 22 years (M = 8.40, SD = 2.83) and those aged 23 years and above (M = 8.43, SD = 2.74) demonstrated comparable scores. Similarly, the t-test for Surface Strategy showed a non-significant difference (t(54) = -1.02, p = .31) between respondents aged below 23 years (M = 11.24, SD = 2.85) and those aged 23 years and above (M = 11.43, SD = 3.16).

The t-test for Deep Approach indicated a non-significant difference (t(54) = -0.86, p = .39) between respondents aged up to 22 years (M = 31.60, SD = 5.79) and those aged 23 years and above (M = 33.64, SD = 5.92). Lastly, the t-test for the Surface Approach showed a non-significant difference (t(54) = -1.57, p = .12) between the two age groups. Respondents aged up to 22 years (M = 19.64, SD = 5.06) and those aged 23 years and above (M = 19.86, SD = 5.10) exhibited similar scores.

The results suggested that the difference in the mean of the various dimensions of approaches to learning across age groups is not significant resulting in the acceptance of the null hypothesis (H01).

Gender and Approach to Learning

Table 4 outlines the outcomes of independent sample t-tests examining potential differences in approach to learning between male and female respondents in learning SRS.

The t-test for Deep Motive indicated a non-significant difference between male (M = 17.00, SD = 3.16) and female (M = 16.52, SD = 3.33) respondents, with t(54) = 0.47, p = .64. Similarly, for Deep Strategy, the t-test revealed a non-significant difference (t(54) = 1.56, p = .12) between male (M = 16.64, SD = 3.39) and female (M = 15.07, SD = 3.22) respondents. The t-test for Surface Motive suggested a marginally significant difference (t(54) = 1.96, p = .05) between male (M = 9.64, SD = 3.34) and female (M = 8.00, SD = 2.48) respondents. For Surface Strategy, the t-test showed a non-significant difference (t(54) = 1.85, p = .07) between male (M = 12.50, SD = 3.18) and female (M = 10.88, SD = 2.72) respondents.

The t-test for Deep Approach indicated a non-significant difference (t(54) = 1.14, p = .26) between male (M = 33.64, SD = 5.97) and female (M = 31.60, SD = 5.77) respondents. However, for the Surface Approach, the t-test suggested a significant difference (t(54) = 2.17, p = .03) between male (M = 22.14, SD = 5.59) and female (M = 18.88, SD = 4.61) respondents.

The results indicated that, for most dimensions, there were no significant gender-based differences in the approaches to learning SRS among MSW students. However, a notable exception was the Surface Approach, where females showed significantly lower scores than males. This suggested that males tend to adopt surface learning approach compared to their female counterparts, and therefore, this study rejected H02 only in the case of the Surface Approach.

Family Type and Approach to Learning

Table 5 outlines the findings of independent sample t-tests examining potential differences in approach to learning between respondents from nuclear families and those from other family types in learning SRS.

For Deep Motive, the t-test indicated a non-significant difference between respondents from nuclear families (M = 16.56, SD = 3.23) and others (M = 17.13, SD = 3.64), with t(54) = -0.45, p = .66. The t-test for Deep Strategy revealed a non-significant difference (t(54) = -1.08, p = .29) between respondents from nuclear families (M = 15.27, SD = 3.32) and others (M = 16.63, SD = 3.11). The t-test for Surface Motive suggested a marginally significant difference (t(54) = -1.78, p = .08) between respondents from nuclear families (M = 8.15, SD = 2.82) and others (M = 10.00, SD = 2.00). For Surface Strategy, the t-test showed a non-significant difference (t(54) = -1.02, p = .31) between respondents from nuclear families (M = 11.13, SD = 2.99) and others (M = 12.25, SD = 2.19).

The t-test for Deep Approach indicated a non-significant difference (t(54) = -0.86, p = .39) between respondents from nuclear families (M = 31.83, SD = 5.89) and others (M = 33.75, SD = 5.55). Also, for the Surface Approach, the t-test suggested a non-significant difference (t(54) = -1.57, p = .12) between respondents from nuclear families (M = 19.27, SD = 5.18) and others (M = 22.25, SD = 3.11).

The results indicated that, for all dimensions, there were no significant differences in the approaches to learning SRS between respondents from nuclear families and those from other family types in learning SRS. Therefore, this study accepted H03.

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Discussion

The exploration of learning approaches among first-year full-time social work post-graduate students in Chennai provides critical insights into the dynamics of curriculum assimilation in the context of social work. The study, employing the Study Process Scale, brings forth a comprehensive understanding of how students engage with the subject SRS. The study identifies a significant portion of students adopting surface approaches to learning. This raises concerns about the depth of assimilation and retention of knowledge. The indication that students might focus on meeting minimum course requirements rather than delving into the intricacies of the subject prompts a critical reflection on the need to trigger deep approaches to learning.

Although Lake and Boyd (2015) argue against it, an interesting gender difference arises, particularly in surface approach scores. While both male and female students exhibit low surface motive scores, males score significantly higher in surface approach. This prompts questions about potential underlying factors contributing to distinct learning behaviours among genders.

At the learner’s end, the prevalence of the surface approach may be attributed to last-minute preparations for evaluation, not adopting a structured approach to learning or existing barriers in learning that prevent the assimilation of subsequent knowledge and emphasises the need to devise strategies to stimulate deeper engagement.

Recommendations are three-fold, targeted at educators, learners and the education system. Educators can employ active learning methods, case studies, and real-world applications to foster a more profound understanding of the subject matter. Initiatives such as research-focused assignments, collaborative projects, and mentorship programmes can contribute to fostering a culture of inquiry. Similarly, the learners need to be sensitised to learn in-depth and should strive to learn the subject matter not merely from the exam point of view but by appreciating its relevance and application in the social work profession. At the education system level, the screening of prior learning and customisation of learning inputs could help the learners adopt the right approach to learning.

Potential future research could delve into contrasting learning approaches across various academic disciplines, providing valuable insights into how pedagogical strategies differ depending on the field of study. By conducting comparative studies between different types of institutions, we could uncover institutional nuances that may influence learning approaches. By examining the impact of institutional characteristics on learning behaviours, we can inform institutional policies and practices. Exploring different learning environments, such as offline, online, or hybrid modes, could help us understand how the mode of learning affects students’ approaches to learning. This is particularly relevant given the evolving landscape of educational delivery.

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Conclusion

In summary, this study delving into the learning approaches of first-year full-time social work post-graduate students in Chennai illuminates critical facets of learning practices. The prevalence of surface learning underscores the imperative for a pedagogical shift, emphasising deeper engagement strategies. Gender differences in approaches to learning and a potential gap in embracing research-oriented mindsets necessitate targeted interventions. Three-fold recommendations – to the learner, to the educator and to the education system have been put forth. Future research avenues exploring interdisciplinary insights, institutional variances, and adaptable learning environments can further enrich our understanding of approaches to learning in social work education. Overall, this study serves as a compass for learners, educators and institutions to reflect on the study process to foster meaningful learning experiences in the dynamic landscape of social work education.

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Tables

Table 1::

Demographic Profile of Respondents



ParticularsNumber (N)Percentage (%)
Age group (years)
Below 234275.0
23 & Above1425.0
Total56100.0
Gender  
Male1425.0
Female4275.0
Total56100.0
Marital Status
Married00.00
Unmarried56100.0
Total56100.0
Family Type
Nuclear4885.7
Extended23.6
Joint610.7
Total56100.0

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Table 2::

Descriptive Statistics of Dimensions of Approach to Learning



DimensionNS1S2MSDCI @ 95%
Deep Motive5112316.643.2716.64 ±0.856
Deep Strategy582115.463.3015.46 ±0.864
Deep Approach10204332.115.8432.11 ±1.530
Surface Motive55158.412.788.41 ±0.728
Surface Strategy561911.292.9011.29 ±0.760
Surface Approach10113119.705.0219.7 ±1.315

N-Number of items; S1-Minimum Score Obtained; S2-Maximum Score Obtained; M-Mean; SD-Standard Deviation; CI-Confidence Interval


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Table 3::

Independent Sample t-Test Results for Age Group and Approach to Learning



VariablesAge Group (years)NMSDt-valuedfp-value
Deep MotiveBelow 234216.553.26-.4554.66(NS)
23 & above1416.933.41   
Deep StrategyBelow 234215.053.39-1.0854.29(NS)
23 & above1416.712.76   
Surface MotiveBelow 23428.402.83-1.7854.08(NS)
23 & above148.432.74   
Surface StrategyBelow 234211.242.85-1.0254.31(NS)
23 & above1411.433.16   
Deep ApproachBelow 234231.605.79-.8654.39(NS)
23 & above1433.645.92   
Surface ApproachBelow 234219.645.06-1.5754.12(NS)
23 & above1419.865.10   

N-Number of respondents, M-Mean, SD-standard deviation, df-degrees of freedom, NS-Not Significant


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Table 4::

Independent Sample t-Test Results for Gender and Approach to Learning



VariablesGenderNMSDt-valuedfp-value
Deep MotiveMale1417.003.16.4754.64(NS)
Female4216.523.33   
Deep StrategyMale1416.643.391.5654.12(NS)
Female4215.073.22   
Surface MotiveMale149.643.341.9654.05(NS)
Female428.002.48   
Surface StrategyMale1412.503.181.8554.07(NS)
Female4210.882.72   
Deep ApproachMale1433.645.971.1454.26(NS)
Female4231.605.77   
Surface ApproachMale1422.145.592.1754.03*
Female4218.884.61   

N- Number of respondents, M- Mean, SD- standard deviation, df- degrees of freedom, NS-Not Significant;

Significant @ 0.05 level


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Table 5::

Independent Sample t-Test Results for Family Type and Approach to Learning



VariablesFamily TypeNMSDt-valuedfp-value
Deep MotiveNuclear4816.563.23-.4554.66(NS)
Others817.133.64   
Deep StrategyNuclear4815.273.32-1.0854.29(NS)
Others816.633.11   
Surface MotiveNuclear488.152.82-1.7854.08(NS)
Others810.002.00   
Surface StrategyNuclear4811.132.99-1.0254.31(NS)
Others812.252.19   
Deep ApproachNuclear4831.835.89-.8654.39(NS)
Others833.755.55   
Surface ApproachNuclear4819.275.18-1.5754.12(NS)
Others822.253.11   

N- Number of respondents, M- Mean, SD- standard deviation, df- degrees of freedom, NS-Not Significant

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References

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