Abstract
The current study examines the morphological knowledge (MK) and self-reported MK self-efficacy of speech-language pathologists (SLPs) and educators in an effort to inform professional development and preservice training related to MK and skills in implementing morphological instruction. This sample of 850 U.S. participants consisted of SLPs (n = 406), English language arts teachers (n = 178), special education teachers (n = 201), and reading specialists (n = 65). A survey elicited professional experience, self-efficacy related to the application of MK, and a MK measure. Correlations and analyses of variance (ANOVAs) between the groups that differed by disciplinary backgrounds were conducted to assess differences in MK and MK self-efficacy. The results yielded two key findings: (a) MK differs by profession, with SLPs performing with higher accuracy in morpheme counting and nonword derivation tasks and (b) MK self-efficacy differs by profession with reading specialists rating themselves significantly more confident than all other professions.
Keywords
Historically, researchers have worked closely with policymakers and curriculum developers to ensure that classroom literacy instruction focuses on building the foundational skills of phonemic and phonological awareness. In contrast, preparing educational personnel to build morphological knowledge (MK) skills has been considered to a lesser extent. However, in the last decade, MK has received more attention as a core knowledge area for speech-language pathologists (SLPs; Brimo & Henbest, 2020), as it has become increasingly accepted that, along with phonemic and phonological awareness, MK is an important subskill associated with learning to read (Carlisle, 2000; Colenbrander et al., 2021; Levesque et al., 2018).
Growing recognition of the importance of MK is also evident in many universal standards for English language arts (ELA), which outline what a student should know and be able to do at the end of each grade level. In third grade, for example, the Common Core Standards state that students are expected to use a known root word as a clue to the meaning of an unknown word with the same root (e.g., company, companion) (CCSS.ELA-Literacy.L3.C). In fact, students are expected to understand the meaning of prefixes and suffixes to increase grammatical accuracy and aid in the understanding of unknown words when reading; however, concerns have been raised as to adequacy or frequency of MK instruction that occurs in typical instructional practices (Gabig & Zaretsky, 2013; Henbest et al., 2019).
Among barriers to MK instruction, the existing literature reports a lack of training and preparation in MK instruction for educational personnel (e.g., Good, 2019; Washburn & Mulcahy, 2019) and difficulties implementing MK interventions without extensive training (Colenbrander et al., 2021). Based on a survey study of 105 school-based SLPs, recruited from American Speech-language and Hearing Association (ASHA)’s School-Based Issues Special Interest Group 16, 30% of the SLPs reported that they did not receive training in MK instruction in their preservice training and 17% were uncertain (Good, 2019). Only 56% had received continuing education related to MK. In addition, 67.7% of the SLPs in Good’s study rated themselves as having moderate to low levels of confidence in providing MK instruction. The need for MK training for educational personnel is further substantiated by the findings of Colenbrander and colleagues’ (2021) study, which reported how teaching assistants (TAs) found structured word inquiry (SWI), a type of MK instruction, challenging to deliver. The TAs reported that many of the concepts were new to them, which may have reduced their ability to tailor instruction and feedback to their students.
Given concerns for preparation and training, further examination of professionals’ knowledge of morphology and skills in implementing morphological instruction may be useful in informing professional development and preservice training. In response, the current study aims to examine SLPs and educator’s MK and self-efficacy ratings. Toward this end, we review the role of MK in literacy and the importance of professional knowledge, skill, and self-efficacy when implementing morphology-focused instruction.
Role of Morphological Knowledge in Language and Literacy
First, it is important to note that the terms morphological awareness and morphological knowledge are both used in the literature; however, based on the arguments recently posed by Kirby and Bowers (2018) regarding the fact that unlike morphological awareness, morphological knowledge may vary on the level of explicitness depending on the type of task and/or the skills of the individual being tasked, therefore the term morphological knowledge (MK) will be used throughout this article. Morphological knowledge refers to the understanding of the morphemic structure of words and the ability to combine morphemes or decompose words into different morphemes (Apel, 2014; Carlisle, 1995, 2000; Levesque et al., 2018; Nagy & Townsend, 2012). Morphemes are the smallest units of meaning and include free bases (e.g., mother, book, day, act, run), bound bases, and roots, which often do not stand alone (e.g., “aud” in audiology, audition, audience), and affixes (i.e., prefixes and suffixes). Affixes can be categorized into two types of morphemes: inflectional and derivational. Inflectional morphemes are those that provide additional information about the base word (e.g., quantity, tense, comparisons) but do not change the meaning of the word. Derivational morphemes modify the base they are added to by changing their meaning and/or grammatical word class. For example, when the inflectional plural -s morpheme is added to “book,” the morpheme -s changes the quantity. Contrastingly, when the derivational morpheme -or is added to a base such as act, the grammatical word class is changed from a verb (act) to a noun (actor).
In prior systematic reviews and meta-analyses, researchers have reported that MK instruction and intervention is a useful component in improving language and literacy skills (e.g., Bowers et al., 2010; Goodwin & Ahn, 2010, 2013). Specifically, researchers have noted that MK is beneficial for several subskills of reading, including decoding (Deacon & Kirby, 2004; Nagy et al., 2006; Roman et al., 2009), spelling (Chomsky & Halle, 1968; Deacon & Bryant, 2006), vocabulary (Baumann et al., 2003; Carlisle & Fleming, 2003), and reading comprehension (Kotzer et al., 2021; Vadasy et al., 2006). The impact of MK has been noted not only for typically developing English proficient students but also for students with language-based learning disabilities (Casalis et al., 2004; Elbro & Arnbak, 1996) and English learners (ELs) (Crosson et al., 2019; Crosson & Moore, 2017; Keiffer & Lesaux, 2008).
SLP and Educator Morphological Knowledge and Self-Efficacy
To successfully foster MK and implement effective instruction or intervention, educators (i.e., ELA teachers, special education teachers, and reading specialists) and SLPs must have an explicit understanding of morphology as it pertains to language, the specific morphological structure of the English language, and the overall self-efficacy to implement such instruction (Colenbrander et al., 2021; Washburn et al., 2016). Research indicates that educators and SLPs use their explicit knowledge of language to assess and treat children with language-based learning disorders (Moats, 2004; Piasta et al., 2009). Without in-depth knowledge of morphology, it may be difficult for them to implement evidence-based recommendations. Moreover, effective communication and collaboration between educational personnel is bolstered by common understanding and knowledge.
Most of the prior research on MK of educational personnel has predominantly focused on teachers’ knowledge and skills (e.g., Moats, 1994; Moats & Foorman, 2003; Piasta et al., 2009; Purvis et al., 2016; Washburn & Mulcahy, 2019). In a survey study by Moats (1994), participants were asked to define terms, provide examples, and analyze phonemes and morphemes. Results of this survey indicated that teachers demonstrated little knowledge as it pertained to morphology terminology. For example, they were unable to differentiate between inflectional and derivational morphemes, and just over 25% of the 89 participants were able to count the number of morphemes in simple one- to two-syllable words. In a similar study, Washburn and Mulcahy (2019) examined the MK of general education teachers and special education teachers. They reported that, on average, teachers were able to correctly count syllables and sounds, but had difficulties identifying individual word parts and counting morphemes in words. Furthermore, Washburn and Mulcahy reported that general education teachers were more accurate on MK tasks than special education teachers.
In addition, while explicit MK is important, the self-efficacy of service providers is another aspect that warrants further study. Self-efficacy is defined as one’s ability to organize and execute a task based on one’s perceived ability to complete said task (Bandura, 1977). Self-efficacy influences choices, effort, and attention when selecting and implementing tasks (Sharp et al., 2016). A few studies have examined self-efficacy as it pertains to literacy-related concepts (e.g., Cunningham et al., 2004; Messier & Jackson, 2014; Spear-Swerling et al., 2005), and in one study, Spear-Swerling and colleagues assessed educator self-efficacy as it related to MK. The investigators found that educators performed poorly on MK tasks and self-efficacy ratings were lower for MK concepts than they were for phonological concepts. Moreover, their self-efficacy ratings appeared to be an overestimation when compared with their actual performance accuracy on MK exercises. This finding is particularly concerning considering that Cunningham and colleagues suggest that over-inflated perceptions of skill level may reduce the chances of individuals seeking continuing education opportunities
Finally, there is a need to inform professional development efforts designed for special education teams serving students with language-based learning disorders. Such teams often include the general classroom teacher, special education teachers, SLPs, and reading specialists. Educational personnel receive various preservice and in-service opportunities to support their instruction. Some studies have shown that having more reading-related professional development is related to students’ language and literacy outcomes (McCutchen et al., 2002; Researchers have argued that there is inadequate preparation in concepts related to language and literacy assessment, instruction, and intervention (Porter et al., 2022; Putman & Walsh, 2021; Reid-Lyon & Weiser, 2009). Thus, more research is needed on the knowledge of such concepts, which includes MK, to inform the development of in-service professional development opportunities to support educational professionals’ integration of MK into reading instruction, which ultimately affect language and literacy outcomes of students with and without disorders (Bowers et al., 2010; Goodwin & Ahn, 2013; Kirby & Bowers, 2018).
Research Aims
In response to the current gaps in the literature, the present study aimed to describe the perceptions, knowledge, and skills on MK tasks of educators and SLPs regarding the role of MK instruction. The current study is driven by the following questions:
Is there a significant difference in MK (measured as morpheme counting and nonword derivation task accuracy) between groups of educational personnel who differ in disciplinary background (ELA teachers, SLPs, special education teachers, and reading specialists)?
Are there group differences in self-efficacy ratings between groups who differ in disciplinary background?
Is there a relation between participants’ self-efficacy ratings and MK measured as morpheme counting and nonword derivation task accuracy)?
Method
Instrument Development
During the drafting phases of the survey, it was piloted with a group of individuals including a doctoral advisor (n =1), doctoral students (n = 6), and SLPs (n = 5). Their feedback was used to finalize the survey. The pilot responses were not included in the final database. The survey consisted of four components: (a) participant demographics, (b) professional experience, (c) self-reflection on knowledge and skills, and (d) a MK measure.
Demographics and professional experience
The demographics component of the survey consisted of eight questions. This set of questions sought information such as type of profession (i.e., ELA teacher, special education teacher, SLP, or reading specialist), years of experience, state where they practice, and grade they teach or treat. This information was used to describe the sample and to also examine relations between responses and participants’ profession.
Self-efficacy
This section sought information on the participants’ MK self-efficacy. The five self-efficacy questions were adapted from a previous study which assessed professional self-efficacy related to vocabulary instruction (Brown, 2019). The original questions were adapted to shift focus from vocabulary to MK. Participants were asked to reflect on their understanding of morphology and MK while answering questions related to assessment, intervention, and role of MK in language and literacy development. The questions were presented as a matrix table and included five self-efficacy rating questions. Participants used a 5-point Likert-type scale to respond: 1 = strongly agree, 2 = somewhat agree, 3 = neutral, 4 = somewhat disagree, and 5 = strongly disagree. An example question was, “I feel confident in my ability to determine appropriate morphological knowledge goals for children with limited language and literacy skills.” Cronbach’s alpha for the five-item self-efficacy scale was .89, suggesting very good internal consistency reliability for the scale (Pallant, 2020).
MK measure
The first author developed a MK measure which consisted of 21 open-ended questions and nine multiple-choice exercises. This section asked participants to answer questions related to different morphological concepts. Prior to beginning the MK measure, the participants were provided with definitions for MK and morpheme. The first 10 questions asked the participants to count the number of inflectional and derivational morphemes in words and phrases. The decision to include morpheme counting tasks was based on the wide use of such tasks when assessing education professionals’ morphological skills (e.g., Moats, 1994; Washburn & Mulcahy, 2019). The words and phrases in the section were developed by the first author and they sought to include a balanced number of single and multimorphemic words. The number of morphemes in the words section (n = 6) ranged from 1 to 4 morphemes. The number of morphemes in the phrases (n = 4) ranged from 3 to 7 morphemes. An example was, “How many morphemes (including free, bound, derivational, or inflectional) are in the phrase “SHE LIKES TO EAT WATERMELON?” This subset of the 10-item morpheme counting questions had a Cronbach’s alpha of .84, suggesting good internal consistency reliability for the scale (Pallant, 2020).
The final nine questions of the survey involved nonword derivation, a task commonly used as a measure of MK (Mahony, 1994). The rationale for inclusion of a non-word derivation tasks in this particular survey was based on the use of such tasks in the relevant literature (e.g., Kieffer & Lesaux, 2012; Tighe & Schatschneider, 2015). The nonword derivation items included in the survey were from a measure used in previous research (Foorman et al., 2021). Participants were asked to read a sentence (e.g., “The meeting was very _________”) and complete the sentence by choosing one of four pseudowords that has an appropriate derivational affix (e.g., “(a) lorialize (b) lorialism (c) lorial (d) lorify”). Cronbach’s alpha in previous research was reported to be .76 (Foorman et al., 2021). In the current study, Cronbach’s alpha coefficient was .37.
An additional 11 MK questions were given to participants but were not included in the analyses due to weak internal consistency reliability. To encourage responses and reduce nonresponse error, the final question of the survey prompted participants with the option to submit their email for the chance to be entered in a raffle to win a free iPad mini. Emails were only used to randomly select a winner and were not used to identify participant responses.
Procedures
The university Institutional Review Board (IRB) approved the distribution of the survey link using Qualtrics. A Tailored Design Method was implemented in the design of this study (Dillman et al., 2014). The survey was developed with a population in mind, educators and SLPs who address the language and literacy needs of their students. The recruitment process was split into two phases.
In the first phase, recruitment was conducted through two social media platforms, Instagram and Facebook. The study announcement was posted to Instagram using a page specifically created by the primary investigator to reach SLPs and educators. The distribution of the survey relied mostly on public shares of the post using an anonymous link to the survey through Qualtrics. The survey link remained active on Instagram and Facebook for 3 months. Instagram post insights revealed that the post was shared 83 times and reached a total of 3,294 people. The survey link was shared on Facebook using the first author’s main page and specific Facebook groups. The survey was posted to five different groups created for ELA teachers (n = 2), SLPs (n = 1), reading specialists (n = 1), and special education teachers (n = 1). Members in each group ranged from 11,000 to 64,400. Because the response to the survey link depended on members that were currently active on Facebook during the time of the post, a calculated response rate would likely result in an underestimation.
The second phase of recruitment relied on communication via email. In this phase, the first author aggregated a list of emails provided from a previous study (Brown, 2019) which included 13,686 teachers located in Florida, Georgia, and Alabama. A second list of emails (n = 124) was also created using ASHA’s Advocacy page where contact information for each state’s speech-language hearing association and special education department could be accessed. The email announcement asked recipients to forward the survey link to professionals that met the survey inclusion criteria (i.e., ELA teachers, special education teachers, SLPs, and reading specialists). Qualtrics reported that a total of 282 responses were due to email contact. Low response rates were expected as contact by email often achieves low responses (Dillman et al., 2014). Because distribution relied in large part on the responsiveness of the contacted administrator, a calculated response rate would likely be an underestimation.
Inclusion criteria
Participants who indicated they were within the profession (e.g., ELA teachers, special education teachers, reading specialists, and SLPs) were included in the study. Respondents outside the United States were excluded from the study (n = 44). Finally, participants who indicated that less than 10% of their caseload had language and literacy goals were excluded (n = 7).
Missing data
A total of 1,128 respondents opened the link and initiated the survey. Of those who began the survey, 850 participants (75%) submitted their responses. According to the Qualtrics Response Data Set, 850 submissions were made, but only 650 participants (76%) finished the survey. Qualtrics defines “Finished” submissions (n = 650) as surveys in which the respondent has reached an end point of the survey, where unfinished surveys (n = 200) indicate a submission where the respondent left their survey before reaching an end point and the response was instead closed manually or closed due to an expired session. It is important to note that although 650 finished the survey, as denoted by Qualtrics, there were instances where participants skipped questions or left questions unanswered. Due to the use of sum scores for the analyses, participants who left questions unanswered were dropped from the analyses. Therefore, group sizes vary in the self-efficacy (n = 791), morpheme counting scores (n = 546) and nonword derivation (n = 633) total scores. Descriptive data were included for all participants who submitted the survey (n = 850).
Sample Characteristics
The final sample consisted of 850 participants with varying professional backgrounds (e.g., ELA teachers, special education teachers, reading specialists, and SLPs). On average, the respondents had about 13 years of experience (SD = 9.97) and a large majority (75%) had a master’s degree, while (20%) had at least a bachelor’s degree. This high percentage of participants with master’s degrees was to be expected considering that several states require SLPs, special education teachers, and reading specialists to have a master’s degree. The majority of the survey respondents identified as Caucasian (87%). The survey was distributed broadly; however, approximately a third of the participants reported working in the southeast region (38.6%). Participant characteristics are summarized in Table 1.
Demographics of Participants in the Morphological Study.
Note. ELA = English language arts.
Results
The present study aimed to (a) describe and compare participants’ MK by profession, (b) describe and compare self-efficacy by profession, and (c) determine whether a relationship exists between participant self-reported self-efficacy and MK.
MK by Profession
To answer the first question which examined potential group differences in MK, we first report descriptive statistics of participant performance on each MK task. Descriptive statistics revealed that, as a group, participants completed the 10 Morpheme Counting tasks with 80% accuracy (M = 7.94, SD = 2.47) and the eight Nonword Derivation tasks with 87% accuracy (M = 6.96, SD = 1.15). As a group, participants demonstrated a high percentage of accuracy on both MK tasks. Table 2 provides composite scores by profession.
MK Composite Scores by Profession.
Note. MK = morphological knowledge; ELA = English language arts; SLPs = speech-language pathologists ; SPED = special education teacher; RS = reading specialist.
A one-way between-group analysis of covariance was conducted to compare MK, as measured by performance on non-word derivation tasks, between the different professions, while controlling for years of experience. The independent variable was the professional field (general education, special education, speech-language pathology, and reading specialist) and the dependent variable consisted of the non-word derivation tasks composite scores. Participants’ number of years of experience in their field was used as the covariate in this analysis. Preliminary checks were conducted to ensure that there was no violation of the assumptions of normality, linearity, homogeneity of variances, homogeneity of regression slopes, or reliable measurement of the covariate. After adjusting for the number of years of experience, there was a significant difference between the groups of participants from different disciplines on non-word derivation tasks, F(3, 626) = 2.875, p = .036, partial eta squared = .014. There was not a significant relationship between the years of experience and performance on non-word derivation tasks (p = .334 and partial eta squared value of .001). See Table 3 for the estimated marginal means.
Nonword Derivation Task Accuracy Estimated Marginal Means by Profession.
Note. ELA = English language arts; SLP = speech-language pathologist.
An analysis of covariance (ANCOVA) was attempted to control for years of experience; however, due to violations of the assumption of equal variance, years of experience could not be parsed out across groups using ANCOVA. As a result, an analysis of variance (ANOVA) with Welch corrections was conducted to explore potential differences in morpheme counting between groups that differed in discipline and years of experience. There was a statistically significant difference in morpheme counting for the three discipline groups, F(3, 124.14) = 16.108, p < .001. The effect size, calculated using eta squared, was .03. Post hoc comparisons using Dunnett’s test revealed that the mean accuracy for SLPs (M = 8.59, SD = 1.67) was significantly greater than morpheme counting accuracy for ELA teachers, special education teachers, and reading specialists (see Table 4).
Post Hoc Dunnett’s Correction Multiple Comparison: Morpheme Counting.
Note. Comparisons with SLPs. ELA = English language arts; SLP = speech-language pathologist.
Self-Efficacy by Profession
To address the next research aim which examined potential group differences in self-efficacy, descriptive statistics were first reported to describe participants’ self-efficacy ratings by profession (see Table 5). To further answer the third research question, an ANCOVA was attempted to control for years of experience; however, due to violations of the assumption of equal variance, years of experience could not be parsed out across groups using ANCOVA. As a result, an ANOVA with Welch corrections was conducted.
Self-Efficacy Composite Scores by Profession.
Note. Participants used a 5-point Likert-type scale to respond: 1 = strongly agree, 2 = slightly agree, 3 = neutral, 4 = slightly disagree, and 5 = strongly disagree. Average self-efficacy scores = mean composite score divided by five self-efficacy items. Range: 5–25. ELA = English language arts; SLP = speech-language pathologist; SPED = special education teacher; RS = reading specialist.
The ANOVA revealed significant differences by profession in the self-efficacy summed score, F(3, 230.103) = 8.005, p < .001. The effect size, calculated using eta squared, was .11. Because of the significant difference in the participants’ self-efficacy ratings, post hoc comparisons were made using Dunnett’s test to analyze the differences by profession. Post hoc comparisons revealed significant differences between reading specialists and all other professions, with reading specialists rating their efficacy higher than other professions (refer to Table 6).
Post Hoc Dunnett’s Correction Multiple Comparison: Self-Efficacy Summed Scores.
Note. Comparisons for reading specialist. ELA = English language arts; SLP = speech-language pathologist; SPED = special education teacher; RS = reading specialist.
The mean difference is significant at .05.
Participant Self-Efficacy and MK
Finally, correlations were examined to consider potential relations between participants’ MK knowledge and Self-Efficacy composite scores. No significant associations were found between Morpheme Counting composite scores and Self-Efficacy. A small significant positive correlation was found between participants’ Nonword Derivation composite scores and Self-Efficacy (r = .09, p = .03).
Discussion
In this survey study, professionals reflected on their self-efficacy related to application topics of morphology and completed MK tasks. The results yielded two key findings: (1) MK differs by profession and (2) MK self-efficacy differs by profession. Before discussing the key findings of this study, we will first address the relation between self-efficacy and MK.
The MK findings of the present study are consistent with those of previous studies related to the knowledge gaps demonstrated by educators (i.e., ELA teachers and special education teachers) and language and literacy specialists (i.e., SLPs and reading specialists) with regard to language concepts (Purvis et al., 2016; Washburn et al., 2016). Other studies have assessed educator’s (Moats, 2009; Spear-Swearling et al., 2005) and SLPs’ (Good, 2019) competence in counting morphemes in words and phrases and found that professionals had difficulties completing this task with accuracy. This could reflect a lack of knowledge in terminology (syllables vs. morphemes) or skill in the counting of syllables rather than morphemes. Among unique findings was the significant differences between professionals in the nonword derivation task after controlling for years of experience; however, it was beyond the scope of this survey to identify the variables contributing to higher performance.
Professionals’ Self-Efficacy
Although previous research has found that educators and related service personnel rate themselves as having proficient to expert knowledge in topics related to language and literacy (e.g., Cunningham et al., 2004; Spear-Swerling et al., 2005), the professionals in the present study were more likely to rate themselves as moderately confident (“somewhat agree” and “neutral”) rather than expert (“strongly agree”). When considering that the present survey asked professionals to rate themselves with regard to their ability to apply their knowledge of morphology rather than their knowledge of morphological terminology or ability to morphologically analyze words and phrases, then the findings of the present study may align with those of previous studies. For example, Washburn and Mulcahy asked teachers both ability (e.g., related to teacher’s ability to morphologically analyze words) and application (e.g., related to teacher’s pedagogical content knowledge about teaching morphology) questions and found teachers to be more likely to accurately answer application questions than ability questions.
Finally, although the causal relations cannot be derived to explain the self-efficacy scores, it is possible that the different levels of training related to the topic may have affected the participants’ responses. For example, the finding that reading specialists had significantly higher self-efficacy ratings may be partially explained by their training which includes extensive training in the areas of reading development, acquisition, assessment, and instruction, which may include in-depth discussions of morphology (International Reading Association [IRA], 2004).
Contributions of MK to Professional Self-Efficacy
The current finding of a small positive relation between participant self-efficacy and skill substantiates the knowledge base on the relationship between skill and self-efficacy. The small positive correlation may suggest that ability to perform highly on the Nonword Derivation Task may influence the ability to adequately describe the role of morphology in literacy. Although the correlation found in this study was smaller than expected, previous studies have found significant correlations between education professionals’ self-efficacy and their actual knowledge when it relates to language concepts (e.g., Mather et al., 2001; Spear-Swearling, 2005). However, it is important to note that this survey did not explicitly ask professionals to reflect on their own ability to perform on MK tasks, instead it assessed professionals’ self-efficacy ratings of the application of their understanding of morphology and MK. As such, there may have been a disconnect between MK tasks and self-efficacy ratings, which may be a plausible explanation for the lack of significant relations by profession and the small size of the correlation.
Limitations
Although the present study provided insight into the MK of educators and SLPs, caution must be taken when interpreting the results. Although the survey reached several states across the nation, the sample may not adequately reflect educators and SLPs from all regions of the country. Many respondents (approximately 39%) reported being from the southeast region of the United States. As such, it cannot be assumed that trends in the current findings would generalize to other educators and SLPs working in other regions. Another weakness to be considered is the unequal sample sizes by profession. Specifically, reading specialists were under-represented in the current respondent pool. This weakness may also be attributable to the current study’s recruitment methods. Almost half of the survey participants were SLPs (48%), same professional background as the authors, while only 8% of the participants were reading specialists. Similarly, the professions included in this study are not exhaustive and there may be other related professions that may provide further insight.
In addition, the length of the survey could also be considered a weakness of the study given that of the 850 participants who began the survey, only 650 (76%) of them completed the survey in its entirety. Survey length has been previously noted to significantly affect response rates, where longer surveys are not typically completed (Kalantar & Talley, 1999). The survey included a large number of demographics and experience questions that may have exhausted participants’ time allocation prior to reaching the MK tasks.
Future Directions
The findings and limitations of the current study warrant the need for empirical study of the MK and professional self-efficacy related to MK. First, future studies should consider implementing purposeful sampling methods in an effort to have a sample that includes a more diverse pool of educators and language and literacy specialists. In future studies, it may also be beneficial to intentionally oversample reading specialists and/or commit resources for recruiting across the fields of language and literacy instructors. The significant group differences found in the current study point to the need for further research on the potential impact of training and exploration of novel types of in-service and preservice training that may be associated with high MK and foster self-efficacy in the application of MK. The findings compel the need for empirical study to add to the knowledge base on impacts of professional development on MK and the association between MK, quality of MK instruction, and student achievement outcomes.
Implications and Conclusion
Despite the limitations of the current study, we still find value in understanding the knowledge of language concepts, which includes MK, among interdisciplinary education teams to inform the development of in-service professional development opportunities tailored to support educational professionals’ integration of MK into reading instruction, by profession, which ultimately affect language and literacy outcomes of students with and without disorders. Although not all professionals in the current study demonstrated expert levels of MK and self-efficacy, it is important to consider that MK, and the role it plays in language and literacy, is a relatively new area of focus. As such, it is possible that some participants had not received training related to MK due to the previous lack of emphasis on MK historically.
In addition, the differences in professional knowledge and confidence levels as it relates to morphology may call for the implementation of collaborative models between interdisciplinary professional. Collaborative MK instruction is aligned with Darling-Hammond and colleagues (2009) who underscore the need and benefits of implementing collaborative models between educators and related service personnel due to the vast range of expertise among professions. For SLPs seeking guidance, Meaux and colleagues (2020) provide a tutorial and Fumero and Tibi (2020) provide a guide for assessment and implementation of morphological instruction.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The first author (Fumero) is funded by the Bilingual Oral Language and Literacy Development personnel development grant, U.S. Department of Education [grant H325D140068]. The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305L180019 to Florida State University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.
