Self-efficacy of urban preservice teachers.
This study was conducted with 60 preservice teachers to learn about their self-efficacy beliefs and such learning behaviors as effort expenditure, assessment of academic accuracy, and time and study environment management strategies in an educational psychology course. Results indicated that preservice teachers who had higher efficacy and used time and study environment management strategies exerted more effort than those with lower efficacy. Also, those exerting more effort were more accurate in assessing their performance capabilities, and subsequently scored higher on their practice tests.
Teacher self-efficacy beliefs positively influence students' learning experiences and academic outcomes. However, teachers face many challenges that hinder their ability to be efficacious and impact their students positively. To better understand these concerns, many investigators are studying teachers' self-efficacy beliefs in general, while others are specifically examining preservice teachers as learners in education courses during their training.  The goal of the present study was to examine the motivational beliefs of preservice teachers as well as how they implement study strategies and accurately assess their learning in an educational psychology course in a teacher training program at an urban college.
Research on Teacher Self-efficacy
The conceptual focus of research on teacher self-efficacy is derived from Bandura's (1997) social cognitive theory. According to Bandura, self-efficacy refers to personal judgments of one's capabilities to perform tasks at designated levels. Self-efficacy can affect choice of activities and environmental settings since people select tasks in which they believe they can succeed. Self-efficacy also affects the amount of effort and length of persistence given to challenging experiences. For example, a person with high self-efficacy may exert much more effort on difficult tasks and persist to overcome obstacles, while someone with low self-efficacy may express doubts about capability and be unwilling to expend effort and time on such tasks.
The construct of teacher self-efficacy was first introduced in the Rand Corporation evaluation studies of factors associated with students' reading performance (Armor et al., 1976; Berman et al., 1977). Teachers' sense of efficacy was reported to have positive effects on the improvement of students' performance. Adapting Bandura's (1996) theory, Gibson and Dembo (1984) developed a measure of teacher efficacy to identify two dimensions: personal teaching efficacy (PE)--belief in one's capability to influence student learning, and teaching efficacy (TE)--belief in one's ability to effect change in student learning, regardless of external, relatively independent factors such as home environment, family background, and parental influence. Using this scale, researchers studied preservice teachers' sense of efficacy (both TE and PE) in relation to their beliefs in pupil control ideology (i.e., level of humanity), teacher autonomy on student motivation, and school bureaucratic orientation. Findings showed that preservice teachers with high PE and high TE also held more humane beliefs toward students, compared with those with low PE and high TE. In addition, teachers who perceived higher self-efficacy seemed to have more humanistic beliefs in classroom management and better lesson presentation, questioning, and classroom management behaviors. In recent studies on preservice teachers' academic learning during their training in relation to understanding and learning scientific concepts (e.g., photosynthesis, inheritance), preservice teachers with higher or positive self-efficacy beliefs in teaching science had fewer misconceptions or alternative conceptions of a scientific topic. 
Research on Calibration
The issue of the accuracy of learners' self-assessment of capabilities in relation to actual academic performance, or calibration, has only recently been examined. Bandura (1997) states that reasonably matched self-efficacy judgments and actions are most desirable, even though higher self-efficacy judgments can enhance motivation to improve future performance. However, while many learners perceived high self-efficacy or capability, their performance did not match this perception; in other words, learners were overconfident when judging their own performance. Thus, most self-efficacy research has focused on learners' perceived capabilities to perform certain activities successfully at pre-determined levels. Current research on self-efficacy has begun to shift to the issue of the calibration of self-efficacy beliefs.  Such studies showed that learners were often overconfident when judging their own academic performance capabilities: middle-school students were poorly calibrated but overconfident when solving math problems, while students who were better calibrated or accurate in their perceived capabilities performed higher. When examining how college students made confidence judgments after answering specific test questions on course materials, researchers found that students expressed higher confidence when answers were correct than when answers were not. Thus, the present study examined the accuracy of preservice teachers' self-assessment by comparing their judgments of the capability to answer test questions correctly and their actual performance on those questions in a college educational psychology course. No study to date has examined this issue with preservice teachers.
Research on Resource Management Strategies
As learners themselves, preservice teachers enroll in various courses in teacher preparation programs to sharpen their knowledge and skills, and further their positive dispositions toward education. However, because preservice teachers lack adequate study skills and are usually not self-regulated learners, introducing them to various learning theories in educational psychology is not enough. Ultimately, one goal of an educational psychology course in teacher education programs is to ascertain that learning theories impact directly on preservice teachers as learners and subsequently on their future students' learning. If preservice teachers acquire and exhibit self-regulated learning behaviors in their own academic pursuits, they may be better able and more willing to model these strategies for their students. One should examine the efficacy of preservice teachers in conjunction with investigating their resource management strategies (i.e., effort expenditure, time and study environment management strategies). As part of self-regulated learning behaviors, strategic planning is crucial to successful academic learning. Learners who are thoughtful about their pre-learning plans, such as adjusting study environments to maximize focus or concentration and allocating study time accordingly, are more likely to have successful performance and persist and exert more effort. Course-referenced perceptions of efficacy among college students were positively and significantly related with effort expenditure. In addition, students who implemented time and study environment management strategies had higher course grades. Thus, it is necessary to examine how preservice teachers use various study and time management strategies when undertaking a course, and how these behaviors relate to other motivational factors such as teacher efficacy and effort expenditure to impact academic performance. 
Research Questions and Hypotheses
The current study attempted to answer the following questions:
(1) How does preservice teachers' self-efficacy influence their effort and calibration judgments?
(2) How do preservice teachers' resource management strategies influence their effort and calibration judgments?
(3) How do preservice teachers' effort and accurate assessment of their capabilities influence their academic performance?
A proposed path model was developed with two exogenous variables (teacher efficacy, time and study environment management) that might have causal relationships on effort judgment and calibration accuracy. First, time and study environment management was shown to be positively correlated with students' final course grades and highly correlated with student effort judgments (Pintrich et al., 1993). It was hypothesized that time and study environment management would be related to preservice teachers' efficacy as well as to effort judgments. Furthermore, since no study has yet examined preservice teachers' efficacy in relation to effort judgments and calibration accuracy, it was hypothesized that those with higher teacher efficacy would exert more effort and be more highly calibrated than those with lower teacher efficacy. Second, Chen (2003) showed that effort judgment was associated with how individuals calibrated their perceived capabilities. Thus, it was hypothesized in the proposed path model that effort judgments would lead to preservice teachers' accuracy prediction as well as performance. Third, following the research on positive influence of accuracy on performance (Bol & Hacker, 2001; Chen, 2003), it was predicted that preservice teachers with higher accuracy judgments would perform better on their tests than those with less accuracy.
Participants were 60 (48 females, 12 males) graduate-level preservice teachers enrolled in the first required course (i.e., educational psychology) in the School of Education at an urban college. All were preparing to be secondary education teachers in middle and high schools for such subject areas as English (n = 41) and science (n = 19). Most participants had no or very limited teaching experience (i.e., substitute teaching).
Practice Test Questions. Participants answered 60 multiple-choice practice midterm questions about course topics such as learning and developmental theories, and classroom management principles typically taught in an educational psychology course. The 60 questions were adapted from course textbook test banks or developed by the instructor, and then divided into two practice tests of 30 questions each. For Practice Midterm Test 1, participants were asked to make self-efficacy and effort judgments before answering each question. Each correct answer was coded as 8, while an incorrect answer was coded as 1 ; these codes were used to calculate calibration measures. Two days later, participants answered 30 multiple-choice questions on Practice Midterm Test 2, assessing the same concepts and materials in Practice Midterm Test 1.
Ohio Teacher Sense of Efficacy Scale (OTSES). This recent teacher efficacy scale, a 24-item scale developed by Tschannen-Moran and Hoy (2001) (who also evaluated its validity), was used to measure teachers' efficacy of student engagement, instructional strategies, and classroom management.
Effort Judgment Scale. This second instrument measured preservice teachers' judgments of amount of effort exerted before answering each practice midterm question. This scale was a task-specific assessment of students' perceived effort level on particular content or material. Effort judgment items were worded as follows: "How much effort did you put forth to understand this particular material?" Rating scale options ranged from 1 (no effort) to 7 (very much effort).
Self-efficacy Scale. This third instrument measured preservice teachers' judgments of confidence before answering each practice midterm question. This scale was a task-specific assessment of students' perceived capability on particular content or material. Self-efficacy items were worded as follows: "How confident are you to answer this question correctly?" Rating scale options ranged from 1 (no confidence) to 7 (highly confident).
Resource Management Strategies. This fourth instrument was a subscale (8 items) of the Motivated Strategies for Learning Questionnaire (MSLQ), time and study environment management, which measured preservice teachers' judgments of how they keep up with their studying schedules, spend time on studying for target course exams or set aside time and place to study (Pintrich et al., 1991, 1993). To answer these items, preservice teachers were instructed to use their present course as the reference.
Calibration of Self-efficacy. This measurement was calculated using Practice Midterm Test 1 data. Bias (direction of judgment errors) and accuracy (magnitude of judgment errors) measures of self-efficacy calibration were computed according to procedures suggested by Pajares and Graham (1999), Pajares and Miller (1997), Schraw et al. (1993), and Yates (1990). To compute bias, each correct answer was scored as 8 and each incorrect answer as 1. These scores corresponded to the self-efficacy scores ranging from 1 to 8. Thus, a participant who expressed "not at all confident" (1) in answering the question correctly but missed the question (1) received a bias score of 0 (1 minus 1). On the other hand, a participant with the same lack of confidence who correctly answered the question received a bias score of-7 (1 minus 8), indicating underconfidence. Therefore, self-efficacy calibration bias scores ranged from -7 to +7. To compute calibration accuracy, Pajares and colleagues recommended subtracting the absolute value of each bias score from 7, which indicates only the magnitude of the judgment error, with a range of 0 (complete inaccuracy) to 7 (complete accuracy). For the present study, only the calibration accuracy measure was used in the data analysis.
Practice Midterm Test 1 data were used to calculate calibration accuracy and bias, and Practice Midterm Test 2 data to obtain students' performance (dependent or outcome measure). Data were collected during two class sessions separated by two days. In Session 1, preservice teachers first filled out the Ohio Teacher Sense of Efficacy Scale (OTSES) (Tschannen-Moran & Hoy, 2001) and a portion of the MSLQ, Resource Management Strategies (Pintrich et al., 1991, 1993). Then, they received Practice Midterm Test 1 with 30 multiple-choice questions. Self-efficacy and effort scales accompanied each multiple-choice question. Asa group, the participants were asked to make self-efficacy and effort judgments for each practice question before answering it. After rendering judgments for all 30 questions, they were instructed to answer the questions. Two days later, during Session 2, they were asked to answer Practice Midterm Test 2. The results of the two practice tests were then discussed with the preservice teachers as preparation for their actual midterm, which counted toward their course grades.
To compare relations among measures, zero-order correlation coefficients were obtained. See issue website http://rapidintellect.com/AEQweb/win2005.htm
Accuracy was the only variable significantly correlated with performance. Preservice teachers who were more accurate in their confidence level performed higher on their practice test. In addition, those more accurate in their confidence level reported exerting higher effort in understanding the practice test materials and using more time and study environment management strategies. As for teacher efficacy, it was significantly correlated with effort judgments, suggesting that preservice teachers with a higher sense of efficacy also reported exerting more effort when trying to understand test materials. In addition, preservice teachers who reported higher effort judgments also reported using more resource management strategies (i.e., time and study environment management).
Path analysis was ah appropriate statistical technique for analysis to test how the data fit into a specific causal model. The proposed path model, which was evaluated using LISREL 8.5 , revealed a nonsignificant chi-squared value (2, N = 60) equaling 0.59 and a p value of .75; a goodness-of-fit index (GFI) adjusted for degree of freedom (AGFI) equaling .97; and a comparative fit index (CF1) equaling 1.00. After removing nonsignificant paths one at a time and retesting the data fit, the final model included one nonsignificant path from the time and study environment management to performance, based on theoretical support and the improvement of the fit indices in the final model.  See issue website http-//rapidintellect.com/AEQweb/ win2005.htm
Results revealed that preservice teachers' accuracy judgments had a statistically significant direct effect on their practice test performance and accounted for 8.8% of variance. In addition, their effort judgments had a statistically significant indirect effect on their performance mediated by accuracy, suggesting that those with higher effort judgments were also more accurate; this combination led to higher performance. See issue website http://rapidintellect.com/ AEQweb/win2005.htm
Table 2 depicts the decomposition of effects from the path analysis; this is important to show indirect effects that are mediated by variables in the path model, such as effort judgments and calibration accuracy. Teacher efficacy and study environment and time management strategies had very small and non-significant indirect effects on performance. However, these two variables had statistically significant positive direct effects on effort judgments and accounted for 47% of variance. As expected, preservice teachers' effort judgments had a positive direct effect on their calibration accuracy, suggesting that those exerting more effort to understand the course materials were more accurate about their capabilities. Teacher efficacy had a positive indirect effect on accuracy via effort judgment, but the effect was not statistically significant. Interestingly, time and study management had a positive and statistically significant indirect effect on accuracy via effort judgment, suggesting that participants who used effective time and study management strategies were likely to exert more effort and be highly accurate in their capabilities. Path analysis results showed that accuracy not only directly affected performance, but was also a key variable mediating performance.
The present study examined preservice teachers' motivational beliefs, learning behaviors, and performance in an urban college educational psychology course. Specifically, three questions were pose& (1) How does teacher efficacy influence effort expenditure and calibration judgments? (2) How do resource management strategies (i.e., time and study environment strategies) influence effort and calibration? and (3) How do these mediating measures (effort expenditures and calibration judgments) impact their own academic performance?
The first two research questions, which tested the influence of teacher efficacy and time and study environment management strategies on effort judgments, were confirmed. Teacher efficacy was found to be highly correlated with effort judgments. Preservice teachers who believed they had a positive influence on their students and were confident about their instructional effectiveness were also more willing to exert effort in learning the educational psychology topics. Research on efficacy (Zimmerman, 2000) supports the findings, specifically that efficacious learners ate more likely to expend more effort and persistence in learning a task, even when encountering obstacles in the process. The time and study environment management strategies measure was also significantly and positively correlated with effort judgments. Using path analysis, the present researchers found that teacher efficacy and time and study management strategies directly impacted the preservice teachers' effort expenditure. Participants who believed themselves capable of a positive impact on students and who used time and study environment management strategies also exerted greater effort in understanding the materials in the course. Both variables explained 47% of variance in effort judgments. As the literature on self-regulated learning and study strategies indicates, learners who use effective study strategies (e.g., studying in quiet places, selecting specific strategies to obtain desired goals, being efficacious about capabilities) are more likely to exert greater effort in their academic endeavors (Zimmerman, 1998). Subsequently, learners who exhibit these behaviors and belief systems are predictive of success in their coursework and earn higher grades than those who do not (Pintrich & De Groot, 1990).
Path analysis confirmed that preservice teachers who reported using time and study environment strategies were also more calibrated or accurate about their learning. This variable had significant and positive direct and indirect effects on accuracy judgments. Path analysis showed that via effort judgments, the use of time and study environment management strategies indirectly impacted the participants' accuracy. In other words, those who used more time and study environment strategies were more likely to exert greater effort in understanding a particular topic in the course; subsequently, they were more calibrated in understanding that topic. However, the present researchers did not find teacher efficacy to be a strong or significant predictor of preservice teachers' accuracy judgments, even with a positive indirect effect. One possible explanation was that teacher efficacy was a global measure of the participants' judgments of capability to implement instructional strategies, conduct classroom management skills, and initiate desired student interactions, while the calibration or accuracy measure was task specific, aligning with the educational psychology topics taught in the course.
As for the third question on the mediating effects of effort judgments and calibration accuracy on predicting performance, path analysis showed interesting results. Preservice teachers' calibration or accuracy judgments had a statistically significant direct impact on how they performed on their practice tests. Such results were consistent with prior calibration studies (Bol & Hacker, 2001; Chen, 2003; Pajares & Graham, 1999), in which learners who are more accurate in their own academic learning performed better than those with lower accuracy. Path analysis also showed that effort judgment was a significant mediating variable in predicting performance. The model indicated that participants who used time and study environment management strategies exerted more effort in understanding the test topics. Furthermore, their effort judgments had a statistically significant indirect impact on their practice test performance as mediated by accuracy. In other words, those who expended greater effort were more accurate in judging their capability and subsequently had a positive influence on performance.
The final path model included an insignificant path from time and study environment management to performance because the researchers' decision to include this path was driven by prior studies (McClendon, 1996; Pintrich et al., 1993) as well as by increasing the fit of the model. McClendon (1996) and Pintrich et al. (1991, 1993) showed that the variable of time and study environment management strategies was correlated and predictive of students' course grades. However, the present researchers did not include preservice teachers' final course grades in this study because the surveys gathered only had self-selected codes. Thus, matching the codes with the final course grades was not possible. Consequently, the present researchers did not find the time and study environment management variable to be predictive of the practice midterm test. Another explanation could be that the performance measure was a practice test which covered only certain topics in an educational psychology course; Pintrich et al. (1993) and McClendon (1996) instead used the course grade as the performance measure, which is more comprehensive than the practice midterm test. One suggestion for future studies is to examine how time and study management strategies are used in different learning situations, such as preparing for tests, completing homework assignments or conducting course projects. One limitation of the present study was the use of practice tests rather than actual graded tests that render consequences. Because practice test results did not have any consequences on the final course grades of the preservice teachers, the variables studied here did not show stronger effects on the performance measure.
The present researchers studied preservice teachers' self-efficacy and learning behaviors, such as effort expenditure, assessment of academic accuracy, and time and study management strategies, in an educational psychology course at an urban college. They found that preservice teachers who had higher teacher efficacy and used time and study environment management strategies exerted more effort in understanding the course materials than those with lower teacher efficacy. In addition, those who exerted more effort had more accurate self-assessment of their capabilities in academic performance. Subsequently, participants who were more calibrated performed better than those who were less calibrated. Preservice teachers who acquire self-regulating skills (e.g., use of study time, study environment selection and/or restructuring, setting goals, self-efficacy, seeking assistance, and evaluating one's performance) in their own learning may be willing to model these behaviors in their future classrooms.
Armor, D., Conry-Osequera, P., Cox, M., King, N., McDonnell, L., Pascal, A., Pauly, E., & Zellman, G. (1976). Analysis of the School Preferred Reading Programs in selected Los Angeles minority schools (R-2007-LAUSD). Santa Monica, CA: The Rand Corporation.
Ashton, P. T., & Webb, R. B. (1986). Making a difference: Teacher's sense of efficacy and student achievement. New York: Longman.
Bandura, A. (1996). Self-efficacy in changing societies. New York: Cambridge University Press.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and Company.
Berman, P., McLaughlin, M., Bass, G., Pauly, E., & Zellman, G. (1977). Federal programs supporting educational change, Vol. 7: Factors affecting implementation and continuation (Report No. R-1589/7-HEW). Santa Monica, CA: The Rand Corporation.
Bong, M. (1997b). Generality of academic self-efficacy judgments: Evidence of hierarchical relations. Journal of Educational Psychology, 89, 696-709.
Bol, L., & Hacker, D. J. (2001). A comparison of the effects of practice tests and traditional review on performance and calibration. Journal of Experimental Education, 69, 133-151.
Cakiroglu, J., & Boone, W. J. (2002). Preservice elementary teachers' self-efficacy beliefs and their conceptions of photosynthesis and inheritance. Journal of Elementary Science Education, 14, 1-14.
Chen, P. P. (2003). Exploring the accuracy and predictability of the self-efficacy beliefs of seventh-grade mathematics students. Learning and Individual Differences, 14, 77-90.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.
Dembo, M. H. (2001). Learning to teach is not enough--Future teachers also need to learn how to learn. Teacher Education Quarterly, 28, 23-35.
Gibson, S., & Dembo, M. H. (1984). Teacher efficacy: A construct validation. Journal of Educational Psychology, 76, 569-582.
Henson, R. K. (2002). From adolescent angst to adulthood: Substantive implications and measurement dilemmas in the development of teacher efficacy research. Educational Psychologists, 37, 137-150.
Joreskog, K., & Sorbom, D. (2002). LISREL 8.5: User's reference guide. Chicago Scientific Software International, INC.
Lundeberg, M. A., Fox, P. W., & Puncochar, J. (1994). Highly confident but wrong: Gender differences and similarities in confidence judgments. Journal of Educational Psychology, 86, 114-121.
McClendon, R. C. (1996). Motivation and cognition of preservice teachers: MSLQ. Journal of Instructional Psychology, 23, 216-220.
Pajares, F., & Grabaral., L. (1999). Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology, 24, 124-139.
Pajares, F., & Miller, M. D. (1997). Mathematics self-efficacy and mathematical problem solving: Implications of using different forms of assessment. The Journal of Experimental Education, 65, 213-228.
Pigge, F. L., & Marso, R. N. (1987). Relationships between student characteristics and changes in attitudes, concerns, anxieties, and confidence about teaching during teacher preparation. Journal of Educational Research, 81, 109-115.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33-40.
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ) (Tech. Rep. No. 91-B-004). Ann Arbor, MI: National Center for Research to Improve Postsecondary Teaching and Learning.
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53, 801-813.
Saklofske, D. H., Michayluk, J. O., & Randhawa, B. S. (1988). Teachers' efficacy and teaching behaviors. Psychological Reports, 63, 407-414.
Schraw, G., Potenza, M. T., & Nebelsick-Gullet, L. (1993). Constraints on the calibration of performance. Contemporary Educational Psychology, 18, 455-463.
Stage, F. K., Carter, H. C., & Nora, A. (2004). Path analysis: An introduction and analysis of a decade of research. The Journal of Educational Research, 98, 5-12.
Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17, 783-805.
Woolfolk, A. E., & Hoy, W. K. (1990). Prospective teachers' sense of efficacy and beliefs about control. Journal of Educational Psychology, 82, 81-91.
Woolfolk, A. E., Rosoff, B., & Hoy, W. K. (1990). Teachers' sense of efficacy and their beliefs about managing students. Teaching and Teacher Education, 6, 137-148.
Yates, J. F. (1990). Judgment and decision making. Englewood Cliffs, N J: Prentice-Hall.
Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: Ah analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1-19). New York: The Guilford Press.
Zimmerman, B. J. (2000). Self-efficacy: Ah essential motive to learn. Contemporary Educational Psychology, 25, 82-91.
 See for examples: Ashton & Webb 1986, Cakiroglu & Boone 2002, Dembo 2001, Gibson & Dembo 1984, Henson 2002, Pigge & Marso 1987, Tschannen-Moran & Hoy 2001, Woolfolk & Hoy 1990.
 See for examples: Cakiroglu & Boone 2002, Saklofske, Michayluk & Randhawa 1988, Woolfolk, Rosoff & Hoy 1990.
 See for examples: Bol & Hacker 2001, Chen 2003, Lundeberg, Fox & Puncochar 1994, Pajares & Graham 1999.
 See for examples: Bong 1997, Dembo 2001, McClendon 1996, Pintrich et al. 1991 & 1993, Zimmerman 1998.
 See for examples: Joreskog & Sorbom 2002, Stage, Carter & Nora 2004.
 See for example: Pintrich & De Groot 1990.
Peggy P. Chen, Hunter College, CUNY Hefer Bembenutty, Queens College, CUNY
Chen, Ph.D., is an assistant professor who teaches psychological foundations in the School of Education, and Bembenutty, Ph.D. is an assistant professor who teaches educational psychology in the Secondary Education and Youth Services Department.
|Printer friendly Cite/link Email Feedback|
|Publication:||Academic Exchange Quarterly|
|Date:||Dec 22, 2005|
|Previous Article:||Creating a culture of (in)dependence.|
|Next Article:||Self-assessment-can they?|