Exploring the roles of academic self-concept and perseverance of effort in self-assessment practices

Figures & data

Figure 1. The self-system processes model of motivation prepared by the authors based on Connell and Wellborn (1991) and existing literature of the variables included in this study.

Note. In drawing the arrows from the three research lines (self-assessment, academic self-concept and grit) to learning outcomes, we referred to previous findings in signature research work documented in the three research lines: a) the monograph on the link between self-assessment and learning enhancement (Boud, 1992, 1995); b) the classic review on the multidimensional and hierarchical construct of self-concept (Shavelson et al., 1976) and c) the first paper on grit as individuals’ passion and perseverance of effort for pursuing long-term goals (A. L. Duckworth et al., 2007), respectively. These paths examined in the previous research are expressed in solid lines, whereas those tested in this study are indicated via dashed lines. H1 denotes the perseverance of effort facet of grit will positively predict students’ self-assessment practice in learning situations; H2 denotes the perseverance of effort facet of grit will positively predict students’ perceptions of competence and affect self-concepts in learning situations and H3 denotes students’ perceptions will mediate the relationship between the perseverance of effort facet of grit and SA practice.

Table 1. Summary of key features of the three variables and conceptual links.

Constructs	Key description	Conceptual elaboration	Essential elements
Grit-PE	Perseverance of effort for achieving long-term goals.	Grit-PE involves long-term striving processes, including affective and behavioural efforts to achieve personal goals across situations, including life and learning events. (e.g. A. Duckworth, 2016; A. L. Duckworth et al., 2007; Lam & Zhou, 2022; Silvia et al., 2013)	Personal striving processes
ASC	Self-concept is broadly defined as an individual’s self-perceptions generated from one’s experience and interpretations of his/her environment. It has hierarchical and multidimensional features covering academic domains (academic self-concepts) and non-academic domains (non-academic self-concepts).	Self-perceptions in academic domains include the cognitive aspects of students’ evaluation of their knowledge and competence in learning school subjects. Self-perceptions also have the descriptive and affective aspects of students’ self-perceived enjoyment and interests in learning school subjects; (e.g. Arens et al., 2011; Marsh et al., 1999; Shavelson et al., 1976; Yang et al., 2016; Yang, 2018)	Self-perceptions of one’s own abilities and affect in learning
SA	Self-assessment comprises three major actions/practices: 1) determining assessment criteria, 2) self-directed feedback-seeking from external and internal sources and 3) self-reflection.	SA is conceptualised as a process for learning involving students’ evaluative judgement of their knowledge, skills and performance through the three major actions/practices (as described in the left column). (e.g. Boud, 1995; Yan & Brown, 2017; Yan, 2016, 2020a)	Self-practices of assessment in learning

Figure 2. Path model 1 to test the predictive effects of Grit-PE and ASC on SA.

Figure 3. Path model 2 to test the predictive effects of Grit-PE and the two aspects of ASC on SA.

Table 2. Descriptive statistics, reliabilities and correlations among the variables.

	1	2	3	4	5	6	7	8
1. SA	-
2. SEFM	.881***	-
3. SEFI	.863***	.615***	-
4. SR	.850***	.719***	.542***	-
5. Grit-PE	.516***	.383***	.493***	.446***	-
6. ASC	.607***	.474***	.596***	.483***	.634***	-
7. ASC_COM	.552***	.438***	.527***	.452***	.643***	.902***	-
8. ASC_AFF	.552***	.424***	.555***	.427***	.515***	.916***	.654***	-
No. of items	9	3	3	3	3	4	2	2
Mean	3.01	3.14	2.74	3.16	2.67	2.60	2.50	2.71
SD	.50	.52	.69	.53	.77	.75	.79	.85
Alpha	.89	.78	.84	.79	.87	.88	.79	.92

***p < .001.

Note. SA = self-assessment, SA is the mean score of SEFM, SEFI and SR; SEFM = seeking external feedback through monitoring; SEFI = seeking external feedback through inquiry; SR = self-reflection Grit-PE = the perseverance of effort facet of grit; ASC = academic self-concept; ASC_COM = the competence component of ASC; ASC_AFF = the affective component of ASC.

Table 3. Bootstrap analysis on mediation effects.

				Bias-corrected percentile 95% CI
	Paths	Standardised β	SE	Lower	Upper
Model 1	Grit-PE → ASC → SA	.297***	.062	.184	.427
Model 2	Grit-PE → ASC_COM → SA	.137*	.056	.033	.252
	Grit-PE → ASC_AFF → SA	.153**	.051	.065	.270

***p < .001; ** p < .01; * p < .05.

Notes. SA = self-assessment; Grit-PE = the perseverance of effort facet of grit; ASC = academic self-concept; ASC_COM = the competence component of ASC; ASC_AFF = the affective component of ASC. Bootstrapping analysis was conducted to examine the mediation effects by resampling and replacing the data 5,000 times.

Table 4. Psychometric indicators for the three subscales of SaPS based on CFA and Rasch analyses.

Item Descriptions	Standardised factor loading		Item measures*		SE		Infit MNSQ		Outfit MNSQ
Seeking External Feedback Monitoring (SEFM)	A. This study n = 180	B. Yan’s (2020a) n = 1,416	A	B	A	B	A	B	A	B
1. I check whether I have fully understood the course content by doing past exam papers.	0.83	0.82	−0.35	−0.13	0.12	0.02	0.91	0.83	0.78	0.82
2. I check whether I have mastered the course content by doing extra exercises.	0.93	0.82	−0.38	−0.05	0.12	0.02	0.67	0.90	0.60	0.89
3. I keep track of my progress by recording my performance.	0.53	0.70	0.72	0.18	0.17	0.03	1.17	1.01	1.14	1.01
Average values	0.76	0.78					0.92	0.91	0.84	0.91
Seeking External Feedback Inquiry (SEFI)	A	B	A	B	A	B	A	B	A	B
4. I ask my fellow group members to evaluate my contributions to group work tasks.	0.85	0.79	−0.16	−0.03	0.11	0.03	0.85	0.97	0.80	0.96
5. I ask my friends to tell me how to improve my learning.	0.86	0.75	−0.19	−0.11	0.11	0.02	0.92	1.15	0.95	1.13
6. I ask my teachers to give me feedback about my performance.	0.71	0.72	0.35	0.14	0.15	0.02	1.18	1.11	1.26	1.12
Average values	0.80	0.75					0.98	1.08	1.00	1.07
Self-Reflection (SR)	A	B	A	B	A	B	A	B	A	B
7. When I do exercise, I look at what I got wrong or did poorly on to guide me as to what I should learn next.	0.74	0.83	0.08	0.15	0.12	0.02	0.87	0.80	0.80	0.83
8. As I study, I think about whether the way I am studying is really helping me learn.	0.67	0.81	0.08	0.22	0.12	0.02	1.18	0.90	1.07	0.92
9. I pay attention to my assessment results in order to identify what I can do better next time.	0.85	0.70	−0.16	−0.37	0.17	0.03	0.81	1.12	0.74	1.07
Average values	0.75	0.78					0.95	0.94	0.87	0.94

Table A1. Fit indices for the measurement of the scales.

	Scales used	Description	χ^2 (df)	RMSEA	CFI	TLI	SRMR
CFA Model A1	SA	General factor	119.206 (27)	.138	.791	.722	.081
CFA Model A2	SEFM, SEFI, & SR	Three correlated factors	51.752 (24)	.080	.937	.906	.063
CFA Model A3	SEFM, SEFI, & SR	Second-order	51.752 (24)	.080	.937	.906	.063
CFA Model A4	SEFM and SR items (as SA F1), SEFI as SA F2	Two correlated factors	64.625 (26)	.091	.913	.879	.059
CFA Model A5	Grit-PE	General factor	.000 (0)	.000	1.000	1.000	.000
CFA Model A6	ASC	General factor	2.471 (1)	.090	.992	.955	.011
CFA Model A7	ASC_COM & ASC_AFF	Two correlated factors	2.471 (1)	.090	.992	.955	.011

Note. CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root-mean-square error of approximation; SRMR = standardised root mean square residual; SA = self-assessment; SEFM = seeking external feedback through monitoring; SEFI = seeking external feedback through inquiry; SR = self-reflection; Grit-PE = the perseverance of effort facet of grit; ASC = academic self-concept; ASC_COM = the competence component of ASC; ASC_AFF = the affective component of ASC. In CFA Model A4, two highly correlated items of the affective component of ASC were correlated (r = .733, p < .001). CFA Model A3 was a saturated model.

Table A2. Items of these scales used in the current study with means, SDs and factor loadings.

			Factor loadings
Item Description	M	SD	General factor	Correlated factors
Self-assessment
SEFM
1. I check whether I have fully understood the course content by doing past exam papers.	3.21	.62	.714	.831
2. I check whether I have mastered the course content by doing extra exercises.	3.22	.61	.808	.934
3. I keep track of my progress by recording my performance.	3.00	.66	.600	.531
SEFI
4. I ask my fellow group members to evaluate my contributions to group work tasks.	2.78	.77	.585	.848
5. I ask my friends to tell me how to improve my learning.	2.78	.79	.626	.859
6. I ask my teachers to give me feedback about my performance.	2.66	.83	.342	.705
SR
7. When I do exercise, I look at what I got wrong or did poorly on to guide me as to what I should learn next.	3.14	.60	.745	.740
8. As I study, I think about whether the way I am studying is really helping me learn.	3.14	.67	.600	.667
9. I pay attention to my assessment results in order to identify what I can do better next time.	3.19	.63	.796	.849
ASC
ASC_ COM
10. I learn things quickly in all school subjects.	2.56	.85	.814	.814
11. I get good marks in all school subjects.	2.44	.89	.809	.809
ASC_AFF
12. I enjoy doing work in all school subjects.	2.74	.89	.663	.869
13. I am interested in all school subjects	2.67	.88	.755	.990
Grit-PE
13. I am a hard worker.	2.67	.86	.833	-
14. I finish whatever I begin.	2.68	.85	.725	-
15. I am diligent.	2.64	.87	.937	-

Note. Students rated on a 4-point scale ranging from 1 (strongly disagree) to 4 (strongly agree). SEFM = seeking external feedback through monitoring; SEFI = seeking external feedback through inquiry; SR = self-reflection; Grit-PE = the perseverance of effort facet of grit; ASC = academic self-concept; ASC_COM = the competence component of ASC; ASC_AFF = the affective component of ASC.

Table A3. Bootstrap analysis on mediation effects.

				Bias-corrected percentile 95% CI
Models	Paths	Standardised β	SE	Lower	Upper
A3.1	Grit-PE → ASC → SR	.212**	.077	.062	.366
A3.2	Grit-PE → ASC → SEFM	.245***	.069	.115	.386
A3.3	Grit-PE → ASC → SEFI	.300***	.057	.195	.420
A3.4.1	Grit-PE → ASC →SA (as a latent variable defined by SR, SEFM, SEFI)	.306***	.072	.164	.445
A3.4.2	Grit-PE→ ASC_COM → SA (as a latent variable defined by SR, SEFM, SEFI)	.150*	.062	.030	.274
	Grit-PE→ ASC_AFF → SA (as a latent variable defined by SR, SEFM, SEFI)	.151**	.057	.045	273

***p < .001; ** p < .01; * p < .05.

Note. In examining the significance of the mediation effects, bootstrapping analysis was conducted, resampling and replacing the data 5,000 times.

Table A4. Fit indices for these models (see Table A3) in the supplementary analyses.

Models	Description	χ^2 (df)	RMSEA	CFI	TLI	SRMR
A3.1	Grit-PE → ASC → SR	.000 (0)	.000	1.000	1.000	.000
A3.2	Grit-PE → ASC → SEFM	.000 (0)	.000	1.000	1.000	.000
A3.3	Grit-PE → ASC → SEFI	.000 (0)	.000	1.000	1.000	.000
A3.4.1	Grit-PE → ASC →SA (as a latent variable defined by SR, SEFM, SEFI)	24.774 (4)	.170	.949	.872	.047
A3.4.2	Grit-PE→ ASC_COM → SA defined by SR, SEFM, SEFI	25.927 (6)	.136	.961	.902	.047
	Grit-PE→ ASC_AFF → SA (as a latent variable defined by SR, SEFM, SEFI)

Note. Models A3.1 to A3.3 were saturated models as shown in Appendices. Given this, chi-square and degree of freedom were equal to zero and model fit indices were satisfactory for research purposes.

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