An international comparative study of practicum mentors: Learning about ourselves by learning about others

Teaching and Teacher Education 90 (2020) 103026

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Teaching and Teacher Education journal homepage: www.elsevier.com/locate/tate

An international comparative study of practicum mentors: Learning about ourselves by learning about others Anthony Clarke a, *, Juanjo Mena b a b

Faculty of Education, University of British Columbia, 2125 Main Mall, Vancouver, British Columbia, V6T1Z4, Canada Faculty of Education, University of Salamanca, P
de Canalejas, 169, P.O. Box 37008, Salamanca, Spain

h i g h l i g h t s  The  The  The  The  The

paper paper paper paper paper

involves mentors from six countries: New Zealand; Thailand; China; Canada; Spain; and Australia. represents a shift from largely idiosyncratic within-context analyses to comparative across-contexts analyses. demonstrates the Mentoring Profile Inventory as a metric for comparative work. highlights distinctive features of mentoring contexts that typically remain hidden within those contexts. demonstrates that there are at least three ‘universals’ about mentoring regardless of context.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 January 2019 Received in revised form 11 January 2020 Accepted 14 January 2020 Available online xxx

Teachers who mentor student teachers on practicum play a critical role in teacher education. However, it is widely reported that these teachers are poorly prepared and lack sufficient knowledge for mentoring. And what is known about mentoring is largely drawn from idiosyncratic single-context studies. In an attempt to extend the scope of this knowledge, this study draws on the Mentoring Profile Inventory (www.mentoringprofile.com) to chart how mentors conceive of their work within and across six international contexts. The outcome is a comparative analysis that highlights what is ‘particular’ versus what is ‘general’ across contexts that has not been previously reported. © 2020 Elsevier Ltd. All rights reserved.

Keywords: Mentoring Supervision Practicum Teacher education Comparative education

1. Context and background Practicum mentoring is a special form of teaching situated in the immediacy of the action setting. Indeed, practicum mentors, often called cooperating teachers, are deeply implicated in the development of their profession or as Lave and Wenger (1991) argue: “in the generative process of producing their own future” (p. 57). Unfortunately, the literature also shows that practicum mentors often receive little or no assistance in preparing for this work (Hansford, Ehrich, & Tennant, 2004). This neglect is one of the most persistent challenges in Teacher Education. In the absence of professional development opportunities,

* Corresponding author. E-mail addresses: [email protected] (A. Clarke), [email protected] (J. Mena). https://doi.org/10.1016/j.tate.2020.103026 0742-051X/© 2020 Elsevier Ltd. All rights reserved.

practicum mentors often rely on their own past experience as student teachers to guide their current mentoring practices. Not surprisingly, they often mentor as they were mentored. However, simply replicating ‘the past’ seriously limits what mentors might offer today’s student teachers (Sarason, 1996). And just accepting the status quo is of little comfort to many student teachers who leave the profession within their first five years decrying the inadequacy of their practicum in preparing them for a career in teaching (Buchanan et al., 2013). Attrition rates for beginning teachers range from 5% in European countries, such as Germany and France, up to 30% in places like the €nger, & Carlsson, United Kingdom and America (Lindqvist, Norda 2014; Ronfeldt, Loeb, & Wyckoff, 2013). As Hudson (2007) argues, we continue to squander our considerable investment in preservice teacher education if we neglect one of the most important elements of that experience: the practicum mentors. Underscoring the significance of this inattention is that student teachers

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universally regard their practicum mentors as critical to their success both on practicum and later within the profession (Izadinia, 2015). To address this shortcoming at least two things must happen: (1) individual practicum mentors must better understand why they do what they do so that what motivates or challenges them in this work can be made explicit and available for critical review and reflection; and (2) the teaching profession, collectively, must better understand what might be typically expected or what might be particularly distinctive about the motivations and challenges that practicum mentors encounter in this work so that they can respond accordinglydsomething which is difficult to do in the absence of such knowledge. The former is often taken up in the mentoring literature in terms of within-context studies that shed light on mentoring within single jurisdictions. For example, the literature indicates that commitment to being a practicum mentor arises from at least three sources:  a commitment to pupils in terms of wanting to ensure the best possible teachers for students (Feiman-Nemser, 2001; Kent, 2001);  a commitment to the profession in terms of wanting to give back to and ensure the continuing development of the profession (Kitchel & White, 2007; Sinclair, Dowson, & Thistleton-Martin, 2006); and  a commitment to self in terms of being exposed to new ideas and strategies through engagement with student teachers (Clarke, 2006; Koskela & Ganser, 1998). As such, these commitments represent important motivators for being a practicum mentor and represent a starting point for exploring the beliefs and assumptions that underlie their practice (Ambrose, Bridges, DiPietro, Lovett, & Norman, 2010). However, very little is written about these commitments beyond the general attributions outlined above and, as such, they tend to remain relatively abstract concepts. The latterdto better understand what might be typical versus distinctive about what mentors encounter in particular contextsdrequires cross-context studies. Cross-context studies allow for the identification of issues that might otherwise remain hidden or normalized from a single-context perspective. Borrowing from Van Maanen (1995), cross-context analyses provide the opportunity to ‘make the strange familiar and the familiar strange’ (p. 20) in ways that are not possible with single-context studies. Alexander (2001) suggests that cross-context studies “reveal alongside each jurisdiction’s unique mix of values, ideas and practices, powerful continuities that transcend time and space” (p. 507). Further, crosscontext studies have the capacity to show that what might be claimed as being distinctive in a one context might be less so when compared to other contexts. It is the juxtaposition of these twodwhat is distinctive and what is commondthat is sought in this study to better inform mentoring practices wherever they occur. Comparative analyses of this type allow individual jurisdictions to compare their outcomes to the means of the ‘population at large’ for similar outcomes; in our study, that population is mentors from around the world. For example, if a particular outcome for an individual jurisdiction sits on or close to the mean for the population at large, then that outcome is a relatively common phenomenon across all mentoring contexts and therefore may not warrant particular attention or action. Alternatively, if the outcome for an individual jurisdiction differs from the mean for the population at

large, then something particular is at play in that jurisdiction, with respect to that outcome, and therefore is worthy of closer scrutiny. Such scrutiny might not have been obvious if that outcome had not been placed against the backdrop of the population at large. For example, what might over time have been considered a relatively mundane motivator for mentors in a particular jurisdiction might be revealed through a cross-context study as being highly significant for mentors in that jurisdiction and may have been neglected to the detriment of recruitment efforts in that jurisdiction. Crosscontext studies provide the ability to flag these outcomes within contexts, something that is not possible through other means. However, cross-context studies of mentoring are largely conspicuous by their absence in the literature and with good reason. These studies are inherently more difficult to undertake because they require ready access to multiple jurisdictions. Also, crosscontext studies typically require some sort of ‘common metric’ that will allow for comparisons to be made with a degree of confidence (Alexander, 2001; Hauser, 2016). The cross-context study of practicum mentors undertaken in this paper draws on a metric deliberately developed for this purpose: the Mentoring Profile Inventory or MPI (Clarke, Collins, Triggs, & Neilsen, 2012). As such, this study offers the potential for an important shift in the mentoring literature from what has been largely idiosyncratic withincontext analyses to comparative cross-context analyses. The MPI is a 62-item online instrument that allows mentors to indicate what motivates or challenges them in their work with student teachers. The MPI then renders participants’ responses in terms of eight motivator scales and six challenge scales: Motivator #1 (M1): Renewing the Profession (4 select items) Motivator #2 (M2): Improving My Own Teaching Practices (4 select items) Motivator #3 (M3): Student Teachers Promote Pupil Engagement (4 select items) Motivator #4 (M4): ‘Time Out’ to Monitor Pupil Learning (4 select items) Motivator #5 (M5): Contributing to Teacher Education (4 select items) Motivator #6 (M6): Reminders about Career Development (4 select items) Motivator #7 (M7): Developing a Professional Community (4 select items) Motivator #8 (M8): Mentoring in Classroom Contexts (4 select items) Challenge #1 (C1): Challenges in Guidance and Mentoring (6 select items) Challenge #2 (C2): Inadequate Forms and Guidelines (5 select items) Challenge #3 (C3): Unclear Policies and Procedures (5 select items) Challenge #4 (C4): Concerns about School Advising as a SubSpecialty (5 select items) Challenge #5 (C5): Concerns about STs’ Pre-Practicum Preparation (5 select items) Challenge #6 (C6): Uncertain Feedback and Communication Practices (4 select items) A graphic report, called an Individual MPI Profile, illustrates the degree to which mentors are motivated or challenged for each scale based on a score from 0 to 50. For those wishing to learn more about the psychometric properties of the MPI, a full account is available in Clarke et al. (2012). One important contribution of Individual MPI Profiles is that they provide mentors with a language for talking about and €n, 1987). framing their practice (Cochran-Smith & Lytle, 1999; Scho

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For example, when asked about the impact of the MPI, one Australian mentor noted: I completed the inventory as part of the ‘Australian Institute for Teaching and School Leadership’ (AITSL) professional development modules. … I found it very useful as a relatively quick and succinct means of assessing my own skills, priorities and also direction for improvement. It did drive home the point as to how, when properly done, mentoring a student can actually be an excellent form of professional development for the teacher and an assistance to improving teaching capacity. … It is some years since I have supervised a pre-service teacher and am about to do so in the coming semester so this opportunity was very timely indeed. (Personal communication, Thursday, July 10, 2014) As such, Individual MPI Profiles provide an external prompt, in addition to prompts by local jurisdictions, for thinking about one’s mentoring practice. Another important advantage of the MPI is that Aggregate MPI Profiles can be generated for a cohort of practicum mentors from a particular jurisdiction (e.g., a school district, a state or province, a country, etc.). The significance of Aggregate MPI Profiles is that they allow the MPI to be used as a metric for cross-context comparative analyses of cohorts of practicum mentors. Aggregate MPI Profiles provide the opportunity to discern what might be considered typical (or normal) versus distinctive (or significant) within and across different contexts.

2. The MPI in context Mishler’s rhetorical question, “Meaning in context: is there any other kind” (p. 1) goes to the heart of all research endeavours. With € n (1987) challenges social science reequal poignancy, Scho searchers to think about how they position themselves in relation to that which they are studying: Shall he remain on the high ground where he can solve relatively unimportant problems according to prevailing standards of rigor, or shall he descend to the swamp of important problems and non-rigorous inquiry? This dilemma has two sources, first, the prevailing idea of rigorous professional knowledge, based on technical rationality, and second, awareness of indeterminate, swampy zones of practice that lie beyond its canons. (p 3) The seductive simplicity of readily codified behaviours has implications for the MPI. For example, the preparation of mentors would be greatly simplified if mentoring were to be viewed as instrumental problem solving made rigorous by the application of educational theory without a consideration of context. This positivist approach suggests that research outcomes are generalizable across contexts and require little or no on-site interpretation (Erickson, 1986). In this study, we are cognizant of being vigilant against narrow technical-rational assumptions that cast the teacher-as-technician or mentor-as-mechanic. We have argued elsewhere that the practicum is a complex system and that learning is a dynamic and adaptive network of relationships and engagements (Clarke & Collins, 2007). Further, Biesta and Miedema (2002), remind us that cultivation, not training, is the goal of any educational enterprise. In this sense, this study is about the development of the profession not just professional development, and the MPI and should be viewed as an instrument for prompting local engagement rather than global imperatives (Gore et al., 2017;

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Young, O’Neill, & Mooney Simmie, 2015); in short, the intent of the mentoring instrument used in this study is not for the instrumentalization of mentoring. To this end, it is critical that conversations with knowledgeable others accompany any use of the MPI, particularly in local jurisdictions. If we accept that knowledge is personally constructed, socially mediated, and inherently situated (Brown, Collins, & Duguid, 1989; von Glasersfeld, 1989a; Wertsch, 1991), then it is important that we honour these characteristics within any context of mentoring if substantive learning is to take place. 3. Method The MPI is currently available free, online, and in eight languages. To date, over 3000 mentors have taken the MPI. This widespread use has allowed for the collective possibilities referred to above. For example, when the number of MPI participants in a particular jurisdiction is large enough, it is possible to generate Aggregate MPI Profiles for a cohort of mentors in that jurisdiction. This study draws on six such cohorts, representing six different countries, to conduct a comparative analysis of what motivates and what challenges mentors across contexts. This analysis allows us to better understand what might normally (or generally) be expected of mentors across settings and also to better discern what might be particularly distinctive within settings. To do so, this study is guided by three research questions: RQ #1. What motivates and challenges practicum mentors within countries? RQ #2. How do the motivators and challenges differ across countries? RQ #3. Do countries group in any significant way in terms of what motivates or challenges mentors? Each question has its own associated research method pathway (Table 1). The institutional affiliation for the mentors in this study (n ¼ 1828) is as follows: Auckland University, New Zealand (n ¼ 178); Kasetsart University, Thailand (n ¼ 170); Northeast Normal University, China (n ¼ 257); University of British Columbia, Canada (n ¼ 544); University of Salamanca, Spain (n ¼ 171); and Wollongong University, Australia (n ¼ 508). Using each of these as country cohorts, a series of tests were conducted. For the sake of simplicity, the results arising from the analysis will be attributed to the country from which each practicum mentor sample is drawn. However, it must be acknowledged that the results, although designated as coming from a particular country, in effect represent practicum mentors in the area of the country from which the sample is drawn. First, normality and homogeneity tests were conducted to determine the distribution and variance for the overall and individual country samples because the parameters of the population for all the countries was not known. The Kolmogorov-Smirnoff normality test showed that the distribution for every country for the motivator scales had values of p > .05 which indicates that the dataset was not different markedly from a normal distribution. However for the challenge scales, countries such as Australia, Canada and New Zealand had values that were significant (p < .05) and therefore not representative of a normal distribution. Since the sample size for these countries is sufficiently large, this result is not critical in terms of the overall purpose of the study. Second, a test of homogeneity was conducted to check that all countries had the same variance thus being suitable for comparative analysis. This is the first step in conducting the ANOVA to check whether the null hypothesis does not differ between variances across groups

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Table 1 Three research questions and associated research method pathways. Research Questions

Hypothesis formulation

Research Approach

RQ#1 What motivates and challenges practicum mentors within countries?

Not applicable

Descriptive  Descriptive statistics analysis (e.g., mean score and SD)

(H1 ) Teacher mentors are not equally motivated to perform their practice across the six countries (H0 ) Teacher mentors are equally motivated to perform their mentoring across the six countries (H2 ) Teacher mentors are not equally challenged to perform their mentoring across the six countries. (H0 ) Teacher mentors are equally challenged to perform their mentoring across the six countries

Compare means

Not applicable

Compare means

RQ#2 How do the motivators and challenges differ RQ#2.1 across countries? What motivates mentors in their work with student teachers? RQ#2.2. What challenges mentors in their work with student teachers? RQ#3 Do countries group in any significant way in terms of what motivates or challenges mentors?

Compare means

Statistical Analyses

 One-way ANOVA/Scheffe post-hoc  ROC curve  Normalized score  One-way ANOVA/Scheffe post-hoc  ROC curve  Normalized score

 One-way ANOVA: Homogeneous subsets, (Bonferroni)

Note: n ¼ 1828 for all analyses.

(countries). If the variances are equal then it is advisable to proceed with the ANOVA test accordingly. Leven’s statistic for the motivators was 1.451 (p ¼ .203). For the challenges it was 7.654 (p ¼ .000). This was basically due to the data distribution of the New Zealand sample (mean ¼ 19.98; sd ¼ 5.001) compared to other countries (mean scores above 21 and standard deviations between 6.295 and 7.529). Without this country, the rest of the country variances were equal. Because this assumption was violated, the Games-Howell procedure was used as it permits the use of ANOVA without assuming equal variances (Games & Howell, 1976). Third, descriptive statistical analyses were conducted to obtain information about mean, median, maximum, minimum and standard deviation calculations for each country’s practicum mentor sample. This was essential for conducting subsequent parametric analyses. Fourth, a comparative analysis using two processes was conducted to determine the differences in the mentoring activity among the six  post-hoc test and Receiver countries: one-way ANOVA with Scheffe Operating Characteristic (ROC) test. Each of the above analyses and their implications are described in detail below.  post-hoc test. Statistical comOne-way ANOVA and Scheffe parisons across cohorts using Aggregate MPI Profiles were done with one-way ANOVAs for the 14 scales, where the p < .05 GamesHowell test for post-hoc multiple comparisons was applied to determine which specific cohort means differed from each other and by how much. The Games-Howell procedure does not assume equal variances across groups, hence corrects for unequal sample sizes while remaining sensitive to small differences between means. Throughout, a standard p < .05 alpha level was maintained to determine the significance of differences between means or any pairs of means. Effect size was also calculated by using Etha square (h2) since for ANOVA tests it is recommended to use this calculation instead of Cohen’s delta. Etha square (h2) was calculated as follows (where SSeffect is the sum of squares for the effect and SStotal is the sum of squares for all effects):

h2 ¼

SSeffect SStotal

This value represents how the independent variable (i.e., countries) influences the dependent variable (i.e., motivators and challenges). It is calculated similarly to R squared and the values used to interpret this coefficient are as follows: small (0.01); medium (0.059); large (0.138) (Cohen, 1988; Miles & Shevlin, 2001).

Once the ANOVA was conducted, a post-hoc test was used to ’s forconfirm the differences between countries. For this, Scheffe mula was used (where Xi  Xj is the mean scores of the samples compared, ni and nj represent the sample sizes, and S2w represents variance within groups):


2 Fs ¼ Xi  Xj " !# S2w

1 ni

 n1j

Receiver Operating Characteristic (ROC) curves and Youden index. ROC curves are typically used to determine the usefulness of a test. In this study they were calculated to demonstrate this point by graphically illustrating the differences among countries. This analysis required the ROC calculation to determine the Area Under the Curve (AUC). ROC curves are basically a plot of the True Positive Rates (TPR)dsensitivitydand the False Positive Rates (FPR)d1 minus specificitydfor the possible cut-off points. Each point in the ROC curve depicts a sensitivity-specificity pair that informs a particular threshold. The Youden index was calculated to determine the cut-off pair that shows the higher difference between a given country and the rest of the countries with regard to the MPI motivators and the challenges. The Youden index (J) is the maximum distance (e.g., vertical line) between the arc of the ROC curve and the chance line (e.g., first bisector or diagonal line). In other words, the index represents the difference between TPR and FPR. It is calculated as: J ¼ maxc {Se (c) þ Sp (c)  1} where c stands for all possible values of the criterion variable (Searle, 1971; Efron & Tibshirani, 1993). The resulting cut off-point for this calculation is the optimal cut-off point (c*) where maximum differences are observed. All cut-off points (for both motivators and challenges) were calculated for all countries using Excel and J was identified manually. The ROC curve analysis served to illustrate the ANOVA statistical differences using the representation of a curve but also, and mainly, to mathematically represent the differences under non-parametric estimates. One of the foremost criticisms in social sciences is the use of parametric analyses with ordinal scales (i.e., it is assumed that the answering position towards any item is the same from 1 to 2 as it is from 2 to 3, etc.). Since this is a controversial issue, and experts tend to agree that educational research should be conducted under non-parametric circumstances, ROC curves and

A. Clarke, J. Mena / Teaching and Teacher Education 90 (2020) 103026

associated AUC were generated. Essentially, the AUC is a measure that shows the difference between two groups and it is calculated with the same values as for Mann-Whitney’s U test where paircomparisons were executed between each country sample (TPR) and the remaining international sample, that is, the total sample (n ¼ 1828) minus each country’s sample size (FPR). In short, the AUC ¼ U/(n1  n1 ) where AUC is the area under the ROC curve, U is the statistical value from Mann-Whitney, and n1 and n1 represent the number of participants from each sample group. An area that equals ‘1’ represents a perfect test or, in this study, the strongest difference between a country with regard the international sample, while an area of 0.5 represents no difference at all (i.e., the diagonal line in the graphical representation or the international sample’s n value with which a given country is compared to). The standard error of the AUC was calculated following the method of DeLong, DeLong, and Clark-Pearson (1988). Normalized scores. Current research indicates that percentiles are a preferable method to normalize scores when sharing the results of a survey with the general public. Percentiles are frequently used to report scoring in a test and indicate the percentage of the sample below which a certain number of values would be expected to fall. Subsequently, the dispersion for each scale was adjusted (8 motivators and 6 challenges) to conform to a near-normal distribution using the inverse normal probability distribution function (in this study, Excel’s NORMINV function was used). The inverse normal probability distribution function operates with mean and standard deviation scores and yields values as percentages. As such, the dispersion of the scores (0e50) for each of the scales varied slightly (see Fig. 2). The semi-interquartile ranges (25% and 75%) are highlighted in preference to standard deviations from the mean because most practicum mentors (and other audiences) are more intuitively familiar with percentages than standard deviations. Nevertheless, all tests for statistically significant differences among countries (see below) were performed using standard ANOVA procedures. ). As a part of the ANOVA Homogeneous subset test (Scheffe analysis, a homogeneous subset test was also conducted by using multiple comparisons to see if countries clustered or grouped in any significant way in relation to what motivates and challenges  method practicum mentors. Assuming equal variances, the Scheffe was chosen because it uses F sampling distribution to conduct pairwise comparisons and for determining groups. Reliability and factorial validity. With respect to the MPI and its use in this study, reliability and validity was confirmed in a study by Clarke et al. (2012). The reliability for the eight motivator factors and six-challenge factors resulted in scales with a-reliabilities ranging from 0.76 to 0.91. All scores were above what Nunnally (1978) established as recommended level: a > 0.70. Factorial validity was also calculated. The exploratory factor analysis executed in Clarke et al. (2012) arranged variables into factors using principal components extraction. The KaisereMeyereOlkin (KMO) measure of sampling adequacy was of 0.77 for the motivators and KMO ¼ 0.84 for 44 for the challenge items. All the estimations were also determined by calculating a tetrachoric correlation matrix (Osborne & Fitzpatrick, 2012). Limitations. As with all studies, there are limitations. Two are particularly important in relation to this study. The first limitation is the narrow band of items (n ¼ 62) that constituted the MPI coupled with the very concise description needed to capture each of those items using instruments of this type. These items were reduced from an original set of 400 and the descriptors honed until there were only a handful of words representing each item. This is a limitation faced by anyone creating an instrument and made even more challenging when trying to develop an online user-friendly format. Nonetheless, the MPI underwent three pilot tests before

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the final version was released in 2011 (Clarke et al., 2012). In 2018, the MPI went through a full review (it was completely disassembled and then reassembled) to ensure consistency and clarity across the entire survey. A second important limitation is the difference in the context in which the MPI was first developed and the subsequent contexts in which it was used in this study (Chabowski, Samiee, & Hult, 2017, p. 90). From the outset, an important element in the construction and translation of the MPI was the involvement of international teacher educators from each of the six countries in this study: Australia, Canada, China, New Zealand, Spain, and Thailand. This international effort ensured that the key concepts underpinning the survey were clearly articulated and accurately translated so that the mentors in each of the respective jurisdictions could meaningfully respond to the MPI. Nonetheless we acknowledge that mentoring is not “culturally neutral and may come to symbolize a host of different values driven by underlying assumptions and their meaning, use, and consequences” (Leidner & Kayworth, 2006, p. 359). Thus, the development of international surveys such as the MPI is always an imperfect process and any claims emerging should be tempered accordingly. 4. Results The results detailed below are organized in terms of the three research questions that guided this study: RQ #1. What motivates and challenges practicum mentors within countries? RQ #2. How do the motivators and challenges differ across countries? RQ #3. Do countries group in any significant way in terms of what motivates or challenges mentors?

4.1. RQ #1: what motivates and challenges practicum mentors within countries? Individual MPI Profiles allow mentors to see what motivates or challenges them in their work with student teachers, and the strength or otherwise of that potential attraction or discouragement. Aggregate MPI Profiles provide a similar overview for a cohort of practicum mentors, that is each of the six countries, involved in the comparative analysis. Over 1800 mentors completed the MPI as part of this study; the means and standard deviations within country for the 14 scales are provided in Table 2. The strength of particular motivators or challenges can be readily observed. For example, the motivators scales for Canadian practicum mentors range from a low of 24.18 to a high of 39.26. 4.2. RQ #2: how do the motivators and challenges differ across countries? These scores can be seen in relation to the scores for the overall sample (Table 2, see ‘Overall’) where it can be seen that mentors felt more motivated (mean score of 36.4 out of 50; that is, moderate to significantly motivated) than challenged (mean score of 22.67 out of 50; that is, slightly to moderately challenged). The international sample also indicated that they were strongly motivated by contributing to teacher education (M4: 40.40), renewing the profession (M1: 40.04) and providing mentoring in classroom contexts (M8: 40.28). However, they were less motivated by the opportunity offered by the student teacher’s presence to monitor their pupil learning (M4: 31.11) or promote pupil engagement (M3: 33.8). In short, the international sample of practicum mentors were more

21.12 (7.00) 25.00 (6.18) 32.09 (6.57) 21.28 (6.23) 22.67 (7.50) 21.86 (8.48) 24.11 (8.88) 31.94 (9.26) 21.97 (7.33) 22.87 (8.93) 24.66 (9.23) 25.73 (8.77) 32.83 (8.58) 21.86 (7.83) 24.52 (9.35) 21.88 (9.75) 30.22 (8.73) 31.99 (8.51) 19.82 (8.56) 23.18 (10.1) 40.78 (7.51) 39.11 (7.48) 38.04 (9.24) 35.56 (8.07) 38.67 (8.03) 40.91 (7.55) 40.25 (7.19) 42.53 (6.23) 43.15 (5.77) 40.04 (7.79)

32.40 (9.63) 37.74 (7.53) 34.82 (8.73) 39.54 (6.59) 33.80 (9.33)

27.23 (9.77) 38.27 (7.38) 36.32 (7.47) 36.87 (7.43) 31.11 (10.1)

42.10 (6.06) 39.42 (6.68) 40.96 (5.91) 40.81 (5.94) 40.40 (6.48)

25.28 (9.58) 38.10 (7.86) 32.92 (9.44) 34.32 (8.19) 28.24 (10.5)

40.10 (7.23) 38.90 (7.69) 38.30 (7.25) 38.87 (7.14) 38.64 (7.61)

41.02 (6.12) 40.10 (6.46) 40.31 (6.87) 41.85 (5.16) 40.28 (6.49)

36.23 (5.64) 38.99 (5.78) 38.03 (5.84) 38.87 (5.30) 36.40 (6.00)

18.04 (6.79) 20.08 (6.25) 32.53 (10.12) 22.34 (7.53) 20.79 (8.80)

21.76 (9.85) 28.02 (8.61) 32.35 (8.44) 19.68 (8.42) 23.09 (10.11)

19.32 (8.08) 22.66 (6.86) 30.76 (9.09) 21.95 (7.13) 22.01 (8.66)

21.04 (8.61) 21.70 (6.68) 23.15 (9.41) 21.13 (7.13) 22.25 (8.91) 19.65 (6.85) 20.58 (9.54) 19.40 (7.26) 21.33 (10.11) 19.44 (7.95) 37.19 (7.98) 40.10 (7.82)

Canada (n ¼ 544) New Zealand ( ¼ 178) Australia (n ¼ 508) China (n ¼ 257) Spain (n ¼ 171) Thailand (n ¼ 170) Overall n ¼ 1828

37.56 (8.36) 39.44 (8.21)

32.36 (9.50) 30.03 (8.78)

30.00 (9.57) 24.75 (9.43)

38.75 (6.85) 41.07 (5.82)

24.18 (9.43) 24.56 (9.33)

37.33 (7.69) 38.15 (8.25)

39.26 (7.05) 40.04 (6.10)

34.58 (6.03) 34.77 (5.50)

20.24 (9.30) 18.54 (5.4)

C3. Unclear Policies and Procedures C2. Lack of Forms and Guidelines

C6. Uncertain C4. School C5. PreAdvising Practicum Feedback and Preparation Communication as SubSpecialty Challenges

C1. Challenges in Guidance and Mentoring Mean and SD for Combined Motivators M8. Mentoring in Classroom Contexts M7. Developing a Professional Community M6. Reminders About Career Development M5. Contributing to Teacher Education M4. ‘Time Out’ to Monitor Pupil Learning M3. Student Teachers Promote Pupil Engagement M2. Improving My Own Teaching Practice M1. Renewing the Profession

Motivators Country

Table 2 Means (and standard deviations) for aggregate MPI profiles (motivators and challenge) by country.

21.41 (7.37) 19.87 (4.94)

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Mean and SD for Combined Challenges

6

motivated to be involved in the practicum by the teaching benefits rendered to the student teacher and the profession at large than by the learning benefits rendered to their pupils by the student teachers’ presence. As for the main challenges reported, overall it was the elements of mentoring that were largely ‘out of the practicum mentors’ hands’ that the international sample found most vexing: for example, the lack of substantive pre-practicum preparation (C5: 24.52); unclear policies and procedures (C3: 23.18), and lack of forms and guidelines (C2: 23.09). Here, mentors are asking for greater support and engagement in the practicum by teacher education institutions. While the wording of these challenges might appear quite stark, it reflects the core of a relationship that mentors are seeking between the universities and the schools. There are no easy answers here and each jurisdiction must question how and in what ways these issues are being addressed. For example, would moving to a digital format for much of this type of communication enable or disable the school/university relationship? Would a digital format change the sense of connection or the feeling of belonging for mentors? 4.2.1. Single-factor analysis of variance (ANOVA) and Scheff e posthoc test Further analysis (ANOVA, ROC curves, and normalized scores) reveals different ways in which teachers from various countries are either motivated or challenged in their work as practicum mentors. The ANOVA highlights the statistical differences between motivators and challenges among countries (see Table 3). The motivator (ALL) and challenge (ALL) scales showed significant differences at the level of p < .05: motivators (ALL), F ¼ 33.35, p ¼ .000; challenges (ALL), F ¼ 90.14, p ¼ .000. The effect size in the case of the motivators was medium (h2 ¼ 0.083) whereas for the challenges it was large (h2 ¼ 0.198). This result can be interpreted as 8.3% and 19.8% of the changes (explained variance) in the motivators and challenges variables, respectively, are due to the independent variable (i.e., the six participant countries). The larger major effect sizes were found in M4 (‘Time Out’ to Monitor Pupil Learning; h2 ¼ 0.210) and M6 (Reminders About Career Development; h2 ¼ 0.250). With respect to the challenges, the higher effects were found in C1 (Challenges in Guidance and Mentoring; h2 ¼ 0.205) and C3 (Unclear Policies and Procedures’; h2 ¼ 0.188). This means that around 20% of the explained variance in both challenges scales is accounted for by the countries’ differences. In other words, these two dependent variables are explained in high proportion by just one independent variable (e.g., country). ) for significance showed that Spanish Post hoc analyses (Scheffe challenge scores were significantly higher in relation to the international sample of practicum mentors than any other country (see Table 4). The Spanish mean difference for challenges is above 10 (row 6, columns 2e4) when compared to Canada, Australia, and New Zealand; and 6.9 (row 6, column 5) when compared to China. This shows that, on average, the Spanish practicum mentors feel 20% more challenged in their role as mentors than their international counterparts (and around 14% more challenged than the Chinese practicum mentors). This is a good example of potential insight gained from cross-context studies that is not always possible using within-context studies. In the case of motivators, China and Thailand (columns 4 and 7, respectively) obtained the highest mean difference scores of the practicum mentor sample compared to Canada (M ¼ 4.408, SE ¼ 0.435 and M ¼ 4.294, SE ¼ 0.504, respectively) and New Zealand (M ¼ 4.219, SE ¼ 0.569 and M ¼ 4.105, SE ¼ 0.616, respectively). Broadly it could be said that Chinese and Thai practicum mentors felt almost 10% more motivated than Canadian and

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Table 3 Source of Variation for Motivator and Challenge Scales (between groups, within groups, and overall totals). Source of Variation Motivators MOTIVATORS (ALL) - Between groups - Within groups - Total MOTIVATOR #1 - Between groups - Within groups - Total MOTIVATOR #2 - Between groups - Within groups - Total MOTIVATOR #3 - Between groups - Within groups - Total MOTIVATOR #4 - Between groups - Within groups - Total MOTIVATOR #5 - Between groups - Within groups - Total MOTIVATOR #6 - Between groups - Within groups - Total MOTIVATOR #7 - Between groups - Within groups - Total MOTIVATOR #8 - Between groups - Within groups - Total Challenges CHALLENGES (ALL) - Between groups - Within groups - Total CHALLENGE#1 - Between groups - Within groups - Total CHALLENGE #2 - Between groups - Within groups - Total CHALLENGE #3 - Between groups - Within groups - Total CHALLENGE #4 - Between groups - Within groups - Total CHALLENGE #5 - Between groups - Within groups - Total CHALLENGE #6 - Between groups - Within groups - Total

SS (1)

df (2)

MS (3)

F(4)

p value (5)

Effect size(h2)(6)

5505.50 60155.93 65661.43

5 1822 1827

1101.1001 33.016

33.3500

.000

.0838 (medium)

6508.217 104233.041 110741.258

5 1822 1827

1301.643 57.208

22.753

.000

0.059 (medium)

5576.970 113791.557 119368.528

5 1822 1827

1115.394 62.454

17.859

.000

0.047 (small)

14442.269 144434.550 158876.819

5 1822 1827

2888.454 79.273

36.437

.000

0.091 (medium)

38952.498 146618.180 185570.678

5 1822 1827

7790.500 80.471

96.811

.000

0.210 (large)

3354.243 73408.486 76762.729

5 1822 1827

670.849 40.290

16.650

.000

0.044 (small)

50891.771 152538.791 203430.563

5 1822 1827

10178.354 83.721

121.575

.000

0.250 (large)

2110.398 103784.227 105894.625

5 1822 1827

422.080 56.962

7.410

.000

0.020 (small)

1287.552 75737.513 77025.065

5 1822 1827

257.510 41.568

6.195

.000

0.017 (small)

20131.24 83441.03 103572.28

5 1822 1827

4072.449 45.179

90.140

.000

0.1984 (large)

28997.291 112473.703 110741.258

5 1822 1827

5799.458 61.731

93.947

.000

0.205 (large)

27843.401 158931.286 186774.687

5 1822 1827

5568.680 87.229

63.840

.000

0.149 (large)

34985.631 151112.231 186097.862

5 1822 1827

6997.126 82.938

84.366

.000

0.188 (large)

17901.815 119206.077 137107.893

5 1822 1827

3580.363 65.426

54.724

.000

0.131 (large)

16463.263 143127.028 159590.291

5 1822 1827

3292.653 78.555

41.915

.000

0.103 (medium)

17194.125 128390.421 145584.546

5 1822 1827

3438.825 70.467

48.801

.000

0.118 (medium)

Notes: (1) Sum of squares; (2) Degrees of freedom; (3) Mean square, estimated by the equation ¼ SS/df; (4) Calculated F value according to equation F ¼ MS (between)/MS (within; (5) p-value: if < 0.05 there is significant difference between the two data groups; and (6) Effect size. h2 (Eta squared).

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Table 4 Scheffe Post-hoc Test.

New Zealander counterparts. However, when compared to Spain, both countries were around 2% more motivated (M ¼ 0.96; for China vs. Spain, row 5, column 6; and M ¼ 0.84 for Thailand vs. Spain, row 6, column 7).

4.2.2. ROC curves and the Area Under the Curve (AUC) ROC curves were used in this study to directly compare a classification system, in this case the scores given by country on the MPI Likert-type instrument, according to motivators and challenges that mentors perceived in their practice. This analysis offers particular thresholds that might be important in making decisions about the phenomenon under study. In this study, it is possible to decide at which point particular differences among countries are indicative of strong response patterns. As explained earlier, AUC scores close to 1 represent a stronger difference between a country and the international sample, while a score of 0.5 represents no difference. Main scores are shown in Table 5. High values for Spain and China are worth noting. The ROC for Spain (Fig. 1) can be summarized as follows: a true Positive value of AUC ¼ 0.854 for challenges in the Spanish sample (Table 5: row 7, column 8) with a confidence interval of 0.827e0.881 at 95% (the value 0.854 is the mean score) and a SE ¼ 0.14. This highlights that 85% of the area is under the curve (also see Fig. 1, the uppermost jagged line). Therefore the differences between Spain (n ¼ 171) and

Fig. 1. Spain’s ‘Receiver Operating Characteristic’ (ROC) to determine the Area Under the Curve (AUC) for deciding True Positive Rates (TPR) and the False Positive Rates (FPR).

Table 5 ROC curve analysis: Differences between each country and the overall international sample.

Canada(1) New Zealand (2) Australia (3) China (4) Spain (5) Thailand (6)

Positive (country’s n value)

Negative (international sample’s n value)

Motivators

Challenges

Area (AUC)

J index

p value

SE

Area (AUC)

J index

p value

SE

544 178 508 257 171 170

1284 1650 1320 1571 1657 1658

0.379 0.411 0.483 0.646 0.588 0.636

0.18 0.149 0.058 0.233 0.164 0.206

**0.000 **0.000 0.265 **0.000 **0.000 **0.000

0.14 0.22 0.15 0.18 0.23 0.21

0.425 0.390 0.420 0.630 0.854 0.446

0.129 0.235 0.134 0.247 0.551 0.134

**0.000 **0.000 **0.000 **0.000 **0.000 *0.021

0.15 0.28 0.15 0.17 0.14 0.21

Note: AUC ¼ Area Under the Curve; J index ¼ Youden Index (J): J ¼ Sensitivityc þ specificityc 1. p-value: if < 0.05 (*) or if < 0.01 (**) there is significant difference between the two data groups: e.g. each country (labeled ‘positive’ above) with respect to the remaining total sample (labeled ‘negative’ above); SE¼ Standard error.

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4), respectively. Therefore, the difference in motivation within these countries can be regarded as about 25%e28% less when compared to more highly motivated practicum mentors (e.g., China). In the case of challenges, the AUC values for both Canada and New Zealand (Table 5, rows 3 and 4; column 8) are AUCCan ¼ 0.425 and AUCNZ ¼ 0.390, respectively, meaning that according to the model they feel challenged in a percentage of around 40% when compared to the international sample. But when compared to Spain (highly challenged group, AUCSp ¼ 0.854) they perceive almost half of the difficulties, drawbacks, and limitations in their work as practicum mentors than their Europeans counterparts (e.g., AUCSp - AUCNZ ¼ 0.464). 4.2.3. Normalized scores for motivators and challenges scales by country Specific charts (Figs. 3 and 4) were designed to graphically illustrate the motivator and challenges scale scores, respectively, for each country relative to:

Fig. 2. China’s ‘Receiver Operating Characteristic’ (ROC) to determine the Area Under the Curve (AUC) for deciding True Positive Rates (TPR) and the False Positive Rates (FPR).

all the other countries (n ¼ 1657) can be accounted for in that percentage. In other words, the area that lies under the curve line represents the percentage of randomly chosen pairs from Spain that differ from the pairs chosen from all the other countries. It shows the probability that the test distinguishes different response patterns from Spain when compared to the other five countries. In short, the Spanish practicum mentors are the ones more challenged in their work as mentors. In general terms, as sensitivity (true positives) increases (c ¼ 0.854), it can be argued that more mentors in Spain who were challenged were identified (sensitivity) but, on the other hand, the accuracy of identifying those who were less challenged was limited (specificity). The Youden Index (J) for Spain (sensitivityc þ specificityc 1) in challenges was 0.55 (cut-off point with values of 0.789, sensitivity, and 0.238, specificity, respectively). Any J value above 0.5 indicates a threshold that marks strong differences between the groups and therefore can be considered relevant. For motivators, China (Table 5, row 6, column 4) has the highest True Positive Value out of the six countries when compared with the international sample (AUC ¼ 0.646 with an interval of confidence of 0.611e0.681 at 95%). Chinese practicum mentors are on average more motivated than any of their international counterparts. This is a case of needing to think carefully about the context and the part it plays in explaining this outcome. Might it be the more communal orientation underpinning Chinese society at play here? While beyond the scope of this paper, these are issues that can, in turn, be taken up at the local level. In this case, the ROC curve for China in Fig. 2 illustrates the differences by the noticeably sharper slope (the jagged line that begins higher than the other two lines at the outset) compared to the chance line (the straight diagonal line that represents the international sample to which it is compared). The Youden index for this country shows the maximum difference between sensitivity and specificity (Sensitivity: 0.626; Specificity: 0.393; J ¼ 0.233). Graphically it would be represented as a vertical line from the higher part of the arc to the chance line. Canada and New Zealand represent the lowest scores for motivators among the countries of the sample with TPV ¼ 0.379 (Table 5: row 3, column 4) and TPV ¼ 0.411 (Table 5: row 4, column

(1) the overall international sample of mentors (i.e., n ¼ 1828); and (2) the individual sample of mentors from each country. The far left and right margins display the percentiles indicating the percentage of the population that lies above and below a particular line across the chart. In particular, there are three horizontal shaded lines that run from left to right across each chart. The horizontal shaded line midway through the chart is the point at which 50% of the population falls above the line and 50% falls below the line. The interquartile rangesdthe 25th percentile and 75th percentiledare highlighted by the upper and lower horizontal shaded lines. For example, 10% of the population lies below the 10th percentile line. At the other end of the percentile range, 10% of the population lies above the 90th percentile line. However, it should be noted that all tests of statistically significant differences among countries (see below) were performed using standard ANOVA procedures. The marker (or point) that is used in this study to locate a particular population (either the overall sample or an individual country) on a particular motivator or challenge scale is the mean score for that population on that scale. The mean score is used because it best represents the collective assessment of all respondents for that population. For the overall population sample this point falls on the 50th percentile for all eight motivator scales and all six challenge scales. Subsequently, each scale’s overall population dispersion is adjusted to conform to a near-normal distribution using the inverse normal probability distribution function. As such, the dispersion of the scores (0-50) for each of the scales varies slightly in Figs. 3 and 4 depending upon the scale in question. Please note that the line joining the scores for a particular country has no significance other than to assist readers to visually track the scores for an individual country in comparison to the overall population and other countries. Based on the mean of the overall sample on both charts (i.e., the 50th percentile shaded horizontal line) and the spread of scores recorded by the mentors from all six countries: - scores falling on the 50th percentile are regarded as standard motivators or challenges for mentors; - scores falling 15 percentile points above or below the 50th percentile (i.e., the 65th percentile or 35th percentile) are regarded as either important or, conversely, modest motivators or challenges, respectively; and - scores falling 30 percentile points above or below the 50th percentile (i.e., the 80th percentile or 20th percentile) are

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Fig. 3. Motivator scale scores: Comparative analysis of six countries.

regarded as either exceptional or, conversely, slight motivators or challenges, respectively. In terms of the comparative work undertaken here, the analysis suggests that results that fall on or near the 50th percentile (i.e., standard) are what can be reasonably expected across all contexts. In contrast, our analysis suggests that results that fall on or near the 65th percentile or 35th percentile (i.e., important or modest), or results that fall on or near the 80th percentile or 20th percentile (i.e., exceptional or slight), are noteworthy because they differ markedly from what might be expected across contexts.

4.2.3.1. Individual country motivator and challenge scores in comparison to the overall sample. In the following analysis, country scores that are regarded as noteworthy are reported. The results for motivators compared to the overall sample are as follows:

a) Canada. Of all eight motivators, only two are noteworthy and both are modest motivators: M1 (Renewing the profession) and M6 (Reminders about career development). Both lie at or around the 35th percentile and do not appear to be particularly strong motivators for Canadian mentors.

A. Clarke, J. Mena / Teaching and Teacher Education 90 (2020) 103026

Fig. 4. Challenge scale scores: Comparative analysis of six countries.

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b) New Zealand. Of all eight motivators, only two are noteworthy and both because New Zealanders only indicate a slight interest in them as motivators: M4 (‘Time out’ to monitor pupil learning) and M6 (Reminders about career development). c) Australia. Only one motivator of all eight is noteworthy: M4 (‘Time out’ to monitor pupil learning). This motivator is only of slight interest to Australian mentors. d) China. Three of the eight motivators are noteworthy and all three positively so. Two are important motivators: M3 (Student teachers promote pupil engagement) and M4 (‘Time out’ to monitor pupil learning). One motivator is exceptional: M6 (Reminders about career development). e) Spain. Two of the eight motivators are noteworthy. Both are important motivators for Spanish mentors: M4 (‘Time out’ to monitor pupil learning) and M6 (Reminders about career development). f) Thailand. Four of the eight motivators are noteworthy for the Thai mentors. Three are regarded as important: M1 (Renewing the profession), M3 (Student teachers promote pupil engagement), and M6 (Reminders about career development). One motivator is reported as only being of slight interest: M2 (Improving my own teaching practice). The results for challenges compared to the overall sample are as follows:

a) China. Two of the six challenges scales were particularly noteworthy, both as important for Chinese mentors: C2 (Lack of forms and guidelines) and C3 (Unclear policies and procedures). b) Spain. All six challenge scales were noteworthy for Spanish mentors. Five scales reached the exceptional level: C1, C2, C4, C5, and C6. One scale was reported at the important level: C3 (Unclear policies and procedures).

4.2.3.2. Individual country motivator and challenge scores in comparison to other countries. In this analysis, scale scores are highlighted for individual countries that are distinctively different from other countries. For the motivator scales, there was something noteworthy about all six countries. For the challenge scales, there was something noteworthy about two countries. The results for motivators in comparison to other countries are as follows:

M1 Renewing the Profession. This is a moderate motivator for mentors from all countries except Canada which falls approximately 15 percentile points below any other country. M2 Improving My Own Teaching Practice. In this instance, it is the Thai mentors who do not find this to be a particular motivator for working with student teachers whereas the other countries find it more compelling but not excessively so.

M3 Student-Teachers Promote Pupil Engagement. The most distinctive feature about this scale is that Chinese and Spanish practicum mentors value the way student teachers promote pupil engagement whereas, almost to the same degree but in the opposite direction, the Australian and Canadians indicate that it is considerably less so. M4 ‘Time out’ to Monitor Student Learning. With the exception of Canada (for whom the result almost maps on to the 50th percentile), this scale splits the other five countries in a significant way: Thailand, Spain, and China find it quite compelling; and for Australia and New Zealand it is particularly low in terms of being a motivator, M5 Contributing to Teacher Education. This is one motivator where all six countries are relatively ‘on the same page’ and cluster around the overall mean. M6 Reminders About Career Development. This is another motivator that splits the countries in opposite directions. This motivator is particularly important for China, Spain, and Thailand but of much less significance for practicum mentors from Australia, Canada, and New Zealand. M7 Developing a Professional Community. Similar to M5 above, all six countries cluster about the overall mean. M8 Mentoring in Classroom Context. Similar to M5 and M7 above, all six countries cluster about the overall mean. The results for challenges in comparison to other countries are as follows: It is interesting to note that responses from two of the countries in the comparison, New Zealand and Spain, are particularly distinctive. Both tend to ‘flat-line’ at opposite ends of the challenge scales. For New Zealanders, none of the six challenges are of any great concern: all are regarded as only ‘slight’ challenges (flat-lining around 40th percentile) within their context. On the other hand, Spanish mentors indicate ‘exceptional’ or ‘significant’ challenges with all six challenge scales (flat-lining around the 85th percentile). Unless otherwise reported, all other countries regard each of the challenges as a standard concern (inside 15 points of the 50th percentile). C1 Challenges in Guidance and Mentoring. Compared to all other countries, Spain is the only country to rate this challenge as ‘critical’ (90th percentile). C2 Lack of Forms and Guidelines. This particular challenge stands out as being exceptional in the Spanish context and as important in the Chinese context. C3 Unclear Policies and Procedures. Practicum mentors in both Spain and China find this to be an exceptional challenge in their contexts. C4 School Advising as a Sub-Specialty. This is an exceptional challenge for Spanish practicum mentors. C5 Pre-practicum Preparation. This is an exceptional challenge for Spanish practicum mentors. C6 Uncertain Feedback and Communication. This is as an exceptional challenge for Spanish practicum mentors. 4.3. RQ #3: do countries group in any significant way in terms of what motivates or challenges mentors? Do the countries group in ways that might be anticipated or are

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there surprising groupings that might challenge taken-for-granted  homogeneous assumptions about those contexts? The Scheffe subset test permits the grouping of countries based on the differences found both in the ANOVA and normalized scores, thereby highlighting similar response patterns (homogenous subsets) across countries. In short, the mean scores that are listed under the same subset are not significantly different from each other and therefore can form a statistically defined subset. This provides another way of ‘making the strange familiar and the familiar strange’ and suggests more macro factors are at play (at the country level). In the following discussion, the groupings will be referred to as high-motivated, medium-motivated, or low-motivated, or highchallenged, medium-challenged, or low-challenged. In terms of  homogeneous subset test indicates that motivators the Scheffe there are two significantly different groupings that were clearly delineated based on the harmonic mean of the group sizes (calculated as 239.559 for both motivators and challenges) as being high-motivated or high challenged, medium-motivated or medium-challenged; and low-challenged (see Table 6). New Zealand, Canada, and Australia, as a cluster, emerged as medium-motivated groups. Perhaps given their cultural similarities, this outcome is not particularly surprising. However, somewhat surprisingly, China, Thailand, and Spain formed another cluster: high-motivated. What might the Asian and European contexts have in common that brings about this particular grouping? The two groups showed an alpha of 0.05. Highmotivated practicum mentors are classroom teachers that all contexts strive for and other than speculating on the possibilities at play, a more definitive answer is beyond the scope of this paper. Nonetheless, the results challenge us to think more expansively about motivation at the macro-level.  test confirms three significantly As for challenges, the Scheffe distinct groups (with alpha of 0.05): Canada, New Zealand, Australia and Thailand are clustered as low-challenged: China, as a ‘singleton’ is identified as medium-challenged: and, Spain as another ‘singleton’ is identified as high-challenged. Again, these results provoke us to think more broadly about how challenge is constructed (and experienced) in these groups. In the case of either the motivator or challenge groupings further investigation is necessary to determine the macro factors at play. For example, what is it about the high-motivated group that might serve as a model for others? What is it about the low-challenged group that might similarly serve as a model to others? This prompts a further question in relation to the above results: Is Thailand the envy of all the other countries? On the one hand, it groups with countries that are high-motivated and, on the other hand, it groups with countries that are low-challenged. As the Canadian poet Leonard Cohen famously wrote: “There is a crack in everything, that’s how the light

Table 6 ). ANOVA test: Homogeneous subsets (Scheffe

Group 1 (High)

Group 2 (Medium)

Group 3 (Low)

Level of Motivation

Level of Challenge

Country

Mean

Country

Mean

China Thailand Spain New Zealand Canada Australia

38.985 38.871 38.026 34.766 34.577 36.227

Spain

32.09

China

25.00

Canada Thailand Australia New Zealand

21.417 21.283 21.122 19.876

13

 test has opened up a crack and illuminated gets in.” The Scheffe some curiosities with respect to cohort groupings that are deserving of future inquiry. 5. Conclusion International comparative studies allow us to learn about ourselves by learning about others. Central to this type of learning is a common metric. In this study, the MPI played that role. As noted earlier, it is important to be mindful of the MPI’s limitations (e.g., issues and meanings vary within contexts and can never be fully accounted for by a common metric). With this caveat in mind, each of the claims detailed above (in response to the three research questions) contributes to our knowledge about mentoring in practicum contexts at both local and global levels. For example, the results reveal that the scores for three scales for all six countries map very closely onto the overall sample mean: - M5 (Contributing to teacher education); - M7 (Developing a professional community); and - M8 (Mentoring in classroom contexts). This outcome suggests these scales potentially constitute three international norms for mentoring in practicum settings. This possibility was not known before this study. One implication of this finding is that all jurisdictions can use this as baseline data upon which to structure mentor recruitment, preparation, sustainability, and succession. To neglect one or more of these motivators would be a mistake given their universal prominence. Likewise, other claims have implications that bear greater scrutiny in particular jurisdictions. In particular, this comparative analysis highlights issues in some countries that are not common to other countries and therefore provide potentially useful insight. For example, this can be seen in the Chinese jurisdiction where there is a very significant challenge with respect to ‘unclear policies and guidelines,’ more so than any other challenge identified by Chinese mentors. Therefore, given its prominence, it would seem prudent to review how and in what way policies and guidelines are presented and communicated in this context. On the other hand, a very significant motivator in this jurisdiction (more so than in any other jurisdiction in this study) is “Reminders about career development.’ Thus, in China it would be important to be mindful of this factor and attend to it accordingly in seeking out teachers to serve as practicum mentors. An avenue of pursuit that might be of interest to Chinese teacher educators would be to articulate and expound upon this aspect of mentoring (which is possibly taken for granted) in that setting so that other countries might learn why it is so significant for Chinese mentors. Whether it is the motivators or the challenges, there is something in the results for each jurisdiction that is unlikely to have been recognized as distinctive for that context prior to this study. Each of these elements needs to be taken up in the respective context and explored accordingly. In sum, this international comparative study of practicum mentors is important for three reasons. First, it is important for the identification of issues that might otherwise remain hidden or unrecognized from a single-context perspective. Second, it is important because what might be claimed as being distinctive in a particular context might be shown to be less so when that claim is located against the backdrop the other mentoring contexts. Third, in the case of the mentoring literature, the comparative analyses presented here represent an important shift from what has been largely idiosyncratic within-context analyses to comparative crosscontext analyses. This initial exploration of the MPI’s potential to deepen our understanding of why and how practicum mentors work with student teachers provides a different way of thinking

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about potential investigations at both the macro and micro levels of teacher education. Examples of some countries already embarking on these investigations include China (Lv, Wang, Ma, Clarke, & Collins, 2016), Australia (Nielsen et al., 2017), and Thailand (Faikhamta & Clarke, 2018). With the MPI now in multiple languages, the possibility for others to tap into this potential is only a keyboard away: www.mentoringprofile.com. Acknowledgements The authors of this study woud like to acknowledge the Social Science and Humanties Research Council of Canada (SSHRC) for their support for this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.tate.2020.103026. References Alexander, R. (2001). Border crossings: Towards a comparative pedagogy. Comparative Education, 37, 507e523. Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: Seven research-based principles for smart teaching. John Wiley & Sons. Biesta, G. J., & Miedema, S. (2002). Instruction or pedagogy? The need for a transformative conception of education. Teaching and Teacher Education, 18(2), 173e181. https://doi.org/10.1016/S0742-051X(01)00062-2. Brown, J., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of teaching. Educational Researcher, 18(1), 32e42. https://doi.org/10.3102/ 0013189X018001032. Buchanan, J., Prescott, A., Schuck, S., Aubusson, P., Burke, P., & Louviere, J. (2013). Teacher retention and attrition: Views of early career teachers. Aust. J. Teacher Educ., 38, 112e129. Chabowski, B. R., Samiee, S., & Hult, G. T. M. (2017). Cross-national research and international business: An interdisciplinary path. International Business Review, 26(1), 89e101. https://doi.org/10.1016/J.IBUSREV.2016.05.008. Clarke, A. (2006). The nature and substance of cooperating teacher reflection. Teaching and Teacher Education, 22(7), 910e921. Clarke, A., & Collins, S. (2007). Complexity Theory and the Supervision of Student Teachers on Practicum. Teaching and Teacher Education, 23(2), 160e172. Clarke, A., Collins, J., Triggs, V., & Neilsen, W. (2012). The mentoring profile inventory: An online professional development resource for cooperating teachers. Teaching Education, 23(2), 167e194. Cochran-Smith, M., & Lytle, S. (1999). Relationships of knowledge and practice: Teacher learning in communities. Review of Research in Education, 24, 249e305. https://doi.org/10.2307/1167272. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, New Jersey: Lawrence Erlbaum Associates. https://doi.org/10.1016/ C2013-0-10517-X. DeLong, E., DeLong, D., & Clark-Pearson, D. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44, 837e845. https://doi.org/10.2307/2531595. Erickson, F. (1986). Qualitative methods in research on teaching. In M. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 119e161). New York: Macmillan https://trove.nla.gov.au/version/44840180. Faikhamta, C., & Clarke, A. (2018). Thai Cooperating Teachers’ Motivations and Challenges in Supervising Student Teachers During Their Internship Program. Kasetsart Journal of Social Sciences. Feiman-Nemser, S. (2001). Helping novices learn to teach: Lessons from an exemplary support teacher. Journal of Teacher Education, 52, 17e30. https://doi.org/ 10.1177/0022487101052001003. Games, P. A., & Howell, J. F. (1976). Pairwise multiple comparison procedures with unequal n’s and/or variances: A Monte Carlo study. Journal of Educational Statistics, 1, 113e125. https://doi.org/10.2307/1164979.

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