View in EDS HTML Full Text PDF Full Text

Investigating CS Teacher Licensure in Indiana

Saved in:
Bibliographic Details
Title: Investigating CS Teacher Licensure in Indiana
Language: English
Authors: Koressel, Jacob (ORCID 0000-0001-5867-0224), Ottenbreit-Leftwich, Anne, Jantaraweragul, Katie, Jeon, Minji, Warner, Jayce, Brown, Matthew
Source: TechTrends: Linking Research and Practice to Improve Learning. May 2022 66(3):412-422.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 11
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Descriptors: Teacher Certification, Computer Science Education, Geographic Location, Institutional Characteristics, Ethnicity, Racial Differences, Socioeconomic Status, Teacher Competencies, Student Characteristics, Equal Education
Geographic Terms: Indiana
DOI: 10.1007/s11528-022-00726-9
ISSN: 8756-3894
Abstract: This study aims to investigate the licensure status of computer science teachers in Indiana by examining teacher licensure data as it relates to school locale and school demographics across the state. Results indicate that there is no significant difference in the presence of teachers with CS-Related or Approved licenses across schools with various locale designations and demographic (ethnic and economic) compositions. It is a positive indication that there are no glaring disparities in access to well-prepared CS teachers across student groups. This also provides an opportunity to investigate additional factors that may shed light on future areas of focus.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1336973
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwHCU2rnkHVDevyNiObTTUSGAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDEIM6IY9lck-hs-bfgIBEICBmx5oxOHij3W0HiVdZfSsI6AWKIT7CCFo8fskCfqtNPFP6bnoJkzcDpYsh2WtBveQcLs5__8x2GE8OdotNsOCImBjWGCS47NPF3-JYt0LIukQzwyvwz5cBNT60h0Bg5bhtudu0AdM3G4aMTMb15I1ngrI7pi3zgfJpD17i0CxhDwiP_vaHL0JxXPkKvodN5ex1k-BxNq2zPHyNi8V
Text:
  Availability: 1
  Value: <anid>AN0157152040;ttr01may.22;2022Jun01.05:49;v2.2.500</anid> <title id="AN0157152040-1">Investigating CS Teacher Licensure in Indiana </title> <p>This study aims to investigate the licensure status of computer science teachers in Indiana by examining teacher licensure data as it relates to school locale and school demographics across the state. Results indicate that there is no significant difference in the presence of teachers with CS-Related or Approved licenses across schools with various locale designations and demographic (ethnic and economic) compositions. It is a positive indication that there are no glaring disparities in access to well-prepared CS teachers across student groups. This also provides an opportunity to investigate additional factors that may shed light on future areas of focus.</p> <p>Keywords: Computer science education; Teacher licenses; Equity</p> <hd id="AN0157152040-2">Introduction</hd> <p>Computer science (CS) education has gained significant momentum over the past several years, both within the United States (Code.org et al., [<reflink idref="bib5" id="ref1">5</reflink>], [<reflink idref="bib6" id="ref2">6</reflink>], [<reflink idref="bib7" id="ref3">7</reflink>]) and globally (European Commission/EACEA/Eurydice, [<reflink idref="bib11" id="ref4">11</reflink>]; Nouri et al., [<reflink idref="bib31" id="ref5">31</reflink>]). The impetus behind this momentum has been characterized by workforce demands, as well as the need to develop skills in students that are needed to participate in society (Blikstein & Moghadam, [<reflink idref="bib2" id="ref6">2</reflink>]; CEDEFOP, [<reflink idref="bib4" id="ref7">4</reflink>]; G20, [<reflink idref="bib16" id="ref8">16</reflink>]). Not only is the number of jobs in computing fields expected to increase in the future, but the expected median income is higher than other jobs. Studies have also shown that computing is increasingly connected to non-computing fields (Tissenbaum & Ottenbreit-Leftwich, [<reflink idref="bib41" id="ref9">41</reflink>]; United States Bureau of Labor Statistics, [<reflink idref="bib42" id="ref10">42</reflink>]).</p> <p>Countries around the world have implemented various policies to support and increase CS K-12 education. For example, Australia passed legislation to include CS concepts taught to students in elementary through grade 10 (Oda et al., 2021). In another example, Sweden has required that CS be offered for all students in elementary through secondary schools (Oda et al., 2021). As a strategy to increase access to CS education, many governments have adopted policies to promote CS education, such as requiring high schools to offer CS courses (Code.org et al., [<reflink idref="bib7" id="ref11">7</reflink>]) or partnering with non-profit corporations to offer CS programs to underrepresented populations of students (Neethipudi et al., [<reflink idref="bib29" id="ref12">29</reflink>]). The adoption of such policies has led to large increases in the number of students taking CS courses (Code.org et al., [<reflink idref="bib7" id="ref13">7</reflink>]).</p> <p>Within the United States, most educational policies are enacted at the state level (Fuhrman, [<reflink idref="bib15" id="ref14">15</reflink>]). However, in some instances, state-level student populations are comparable to other nations' student populations (e.g., Texas 2019-2020 K-12 student enrollment = 5,493,940 (Texas Department of Education, [<reflink idref="bib39" id="ref15">39</reflink>]); California 2019-2020 K-12 student enrollment = 6,163,001(California Department of Education, [<reflink idref="bib3" id="ref16">3</reflink>])). As such, state-level systemic changes may be considered similar to national changes in other countries. For example, Gal-Ezer & Stephenson ([<reflink idref="bib17" id="ref17">17</reflink>]) have demonstrated how the United States and Israel have utilized similar models to promote CS education and provide CS experiences to all their students.</p> <p>To support states within the United States in their efforts to broaden participation in K-16 computing, the National Science Foundation has invested in an alliance organization: Equity in Computing Education Policies, Practices, and Pathways (ECEP3). Through the ECEP3 alliance, states work to broaden participation in computing through a Five Stage Model (Ottenbreit-Leftwich et al., [<reflink idref="bib32" id="ref18">32</reflink>]): the establishment of a team, conducting landscape reports, hosting summits and developing a state-wide plan, funding initiatives, and data dashboards.</p> <p>As one example of an ECEP3 state and their journey towards increasing CS K-12 education, Indiana passed legislation in 2018 requiring that all high schools offer at least one CS course each year by the fall of 2021 (S. E. A. 172, [<reflink idref="bib35" id="ref19">35</reflink>]). This started a dramatic increase in high school CS offerings across the state. During the 2017-2018 school year, 10,141 students completed a CS course, which has now increased to 19,377 students 2020-2021 school year (Indiana Department of Education, [<reflink idref="bib26" id="ref20">26</reflink>]). Stakeholders have suggested that this increase was due, in part, to this policy that promotes CS education.</p> <p>However, a dramatic increase in CS education also necessitates a quick increase in teachers to be able to offer these courses, often described as increasing our CS teacher capacity. As CS education is a more recent addition to most countries' curriculums (Falkner et al., [<reflink idref="bib12" id="ref21">12</reflink>]), the focus on CS teacher capacity would be - identifying and creating more teachers capable of teaching computer science. In fact, a European [<reflink idref="bib43" id="ref22">43</reflink>] study by Vahrenhold et al. ([<reflink idref="bib43" id="ref23">43</reflink>]) found that 47 of the European countries/regions had special qualifications for teaching CS, while 30 European countries/regions did not provide information, and seven European countries/regions had no qualifications.</p> <p>Teacher capacity has also been a key focus of the CAPE framework, which emphasizes equity-oriented approaches to analyzing and improving the K-12 computer science education ecosystem (Fletcher & Warner, [<reflink idref="bib14" id="ref24">14</reflink>]). In order to offer CS experiences to all K-12 students, capacity is critical to providing access to computer science education for K-12 students and provides a foundation for refining and improving opportunities. Therefore, it is critical to examine how states are increasing K-12 teacher capacity and how that relates to K-12 CS education offerings.</p> <hd id="AN0157152040-3">Building Capacity for CS Teachers</hd> <p>With the increase in the number of students taking CS, there was a demand for more teachers to teach CS. As CS is relatively new to K-12 education, few existing teachers are certified to teach CS (Ni et al., [<reflink idref="bib30" id="ref25">30</reflink>]). The challenge of needing more qualified teachers is not a new one as science, technology, engineering, and mathematics (STEM) continuously face teacher shortages (Han & Hur, [<reflink idref="bib23" id="ref26">23</reflink>]). These teacher shortages particularly impact schools in rural and urban areas, often struggling to find highly qualified math and science teachers (Pennington McVey & Trinidad, [<reflink idref="bib34" id="ref27">34</reflink>]). Stakeholders have proposed several initiatives to address this problem, such as recruiting individuals with relevant industry experience and/or education (not in teacher preparation) to teach math and science courses in secondary schools. NSF funded initiatives like 100Kin10 have aimed to train large quantities of individuals to be prepared to teach in the STEM disciplines (White House, [<reflink idref="bib40" id="ref28">40</reflink>]).</p> <p>However, the CS teacher capacity problem faces additional challenges compared to those in math and science. As CS is a relatively new content area in K-12, few existing teachers have prior experience with CS. Furthermore, CS experiences are not typically included in teacher preparation programs (Ottenbreit-Leftwich et al., [<reflink idref="bib33" id="ref29">33</reflink>]). Similar to math and science, recruiting individuals with relevant business or industry experience has been a strategy for CS as well. However, many CS careers outside of education offer higher salaries and K-12 schools are often not able to be competitive (DeLyser, [<reflink idref="bib10" id="ref30">10</reflink>]).</p> <p>Efforts have been made in recent years to increase the number of licensed computer science teachers. However, all states have different requirements for certification and authorization of teachers to teach CS (Kim et al., [<reflink idref="bib28" id="ref31">28</reflink>]). Licensing requirements for being able to teach CS have also changed over the past decade. Of the few Indiana high schools that offered CS before 2015, most had a business or career & technical education (CTE) teacher teaching the course. Since the introduction of Indiana's CS standards in 2016, the requirements around who could teach CS have evolved. Initially, teachers could teach CS if they had Computer Education, Business, or certain CTE certifications. In 2019, a policy change added a Computer Science license. Additionally, supplemental authorizations were allowed for teachers with Science, Math, or Technology Education licenses who completed additional CS training or professional development.</p> <p>Across the US, the broadening of CS certification pathways has included expanded licenses. This has resulted in a wide range of teacher certifications and backgrounds for those teaching high school CS courses. While this trend has been necessary to increase the number of CS teachers (Zarch et al., [<reflink idref="bib45" id="ref32">45</reflink>]), it raises questions about how to ensure teachers are prepared to teach high school CS courses.</p> <hd id="AN0157152040-4">Capacity Building in an Equitable Way</hd> <p>In addition to ensuring that teachers are prepared to teach CS, we also need to examine where the most prepared teachers are located. Are the suburban schools with low proportions of economically disadvantaged students more likely to have a knowledgeable, qualified CS teacher? Are rural schools being staffed with teachers who have CS-Related licenses? Are schools with higher minority populations more likely to have CS teachers without CS-Related licenses? As we study CS education, it is critical that we examine the equity of CS education initiatives and practices. Fletcher and Warner ([<reflink idref="bib14" id="ref33">14</reflink>]) developed the CAPE framework to measure and guide progress toward equitable CS education. CAPE looks at Capacity, Access, Participation, and Experience. Specifically, capacity refers to a school or district's ability to offer and teach high-quality computer science courses. Well-prepared and highly-qualified instructors are a key indicator of capacity to offer CS education. By utilizing an equity lens, we can begin to interrogate the data to explore the impact of various pathways to licensure on enrollments and experiences.</p> <p>Therefore, we examined the current state of CS teacher licensure in Indiana in order to understand any relationships between statewide policies and practices and teacher licensing. Specifically, this study examined the following research questions:</p> <p></p> <ulist> <item> What licenses do Indiana CS teachers have who are teaching CS courses?</item> <p></p> <item> What is the relationship between the demographics of a school and CS teacher licenses?</item> <p></p> <item> What is the relationship between socioeconomic status and CS teacher licenses?</item> <p></p> <item> What is the relationship between locale (suburban, urban, rural) and CS teacher licenses?</item> <p></p> <item> What is the relationship between students' race/ethnicity and CS teacher licenses?</item> </ulist> <hd id="AN0157152040-5">Methods</hd> <p></p> <hd id="AN0157152040-6">Research Design</hd> <p>This study examined the relationship between teachers' licenses and student demographics. The teacher licenses for all teachers teaching a computer science course during the 2019-2020 school year within the state of Indiana were solicited from the Indiana Department of Education. The data sources include state-level databases of teacher licensure data and school demographic data from the Indiana Department of Education during 2020. Descriptive statistics were used to describe the number of licenses and disaggregated the data based on locale, ethnicity, and socioeconomic status. In addition, logistic regressions were used to identify contributing factors to the binary response whether a school has a CS-related licensed teacher or not. In particular, the logit model investigates the relationship between the teacher licenses and demographic attributes of schools (locale, economic status, and ethnic compositions). To characterize school locale, the definitions from the CCD (Common Core of Data) were used to categorize locales as city, suburban, town, and rural. The computer science courses that satisfy the requirement for Indiana high schools to offer a computer science course and were included in this solicitation are:</p> <p></p> <ulist> <item> Introduction to Computer Science</item> <p></p> <item> Computer Science I</item> <p></p> <item> Computer Science II</item> <p></p> <item> Computer Science III: Special Topics</item> <p></p> <item> Computer Science III: Software Development</item> <p></p> <item> Computer Science III: Databases</item> <p></p> <item> Computer Science III: Informatics</item> <p></p> <item> Computer Science III: Cybersecurity</item> <p></p> <item> Principles of Computing</item> <p></p> <item> Software Development</item> <p></p> <item> AP Computer Science A</item> <p></p> <item> AP Computer Science Principles</item> <p></p> <item> IB Computer Science Standard Level</item> <p></p> <item> IB Computer Science Higher Level</item> <p></p> <item> Cambridge International A Level Computer Science</item> <p></p> <item> Cambridge International AS Level Computer Science</item> </ulist> <p>A primary selection criteria for this list included having a substantial programming component. We intentionally made sure not to conflate computer science with computer applications or software to remain in alignment with the courses Indiana schools must choose from to be in compliance with the CS course requirement. Therefore, courses like digital citizenship and web design were not included in this study.</p> <hd id="AN0157152040-7">Data Analysis</hd> <p>First, we categorized teacher licenses by those specific to computer science and those most closely related to computer science. These licenses included computer education, computer science, certain workplace specialist permits, business, CTE: Business and Information Technology, and Data Processing. The next category included "approved" to teach CS licenses that were not related to computing, such as Science, Math, and Technology Education. The last category was all other licenses that are not approved to teach computer science, like English, special education, or government. For a full list of licenses, see Table 1 below. This data was reported using descriptive statistics.</p> <p>Table 1 CS-related, approved to teach CS, and not approved to teach CS teacher licenses</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th><p>Computer science related</p></th><th><p>Approved</p></th><th><p>Not approved</p></th></tr></thead><tbody><tr><td><p>Career and Technical Education: Business and Information Technology, Industrial Electronics, Industrial Technology, Precision Machine Technology, Graphic Imaging Technology</p></td><td><p>Business Education: Accounting and Finance, Advanced Business Management, Bookkeeping, Clerical, Entrepreneurship, Marketing, Vocational Business</p></td><td><p>Administration: Building Level Administrator, Cooperative Coordinator, Director of Career and Technical Education, Director of Curriculum and Instruction, Director of Vocational Education, Elementary Administration and Supervision, Secondary Administration and Supervision, Superintendent</p></td></tr><tr><td rowspan="5"><p>Computer Science: Computer Education, Computer Science, Cybersecurity, Programming, 3/D Computer Animation and Visualization, Computer Operations and Programming, Computer Illustration and Graphics, Computer in Design and Production, Networking and Telecommunications, Computer Repair and Maintenance, Computer Technical Support, Data Processing, Electronics Technology, Graphic Imaging Technology, Information Support and Services, Interactive Media, Network Systems, Programming and Software Development, Logistics, Networking, Technology Education</p></td><td><p>Science: Biology, Chemistry, Earth Space Science, Engineering, Life Science, Physical Science, Physics</p></td><td><p>Career and Technical Education: Advanced Manufacturing, Architectural Engineering, Building Trades Technology, Marketing, Occupational Family and Consumer Sciences, Commercial Photography, Driver and Traffic Safety Education, Graphic Arts, Law Enforcement Training, Machine Drafting, Radio/TV Broadcasting/Telecommunications, Computer Aided Design, Visual Arst, Industrial and Technical Lab, Welding Technology</p></td></tr><tr><td rowspan="4"><p>Math: Mathematics</p></td><td><p>English: English as a New language, Journalism, Language Arts, Reading, Speech Communication and Theatre</p></td></tr><tr><td><p>Social Studies: Anthropology, Economics, Geography, Government, Historical Perspectives, Psychology, Sociology, United States History, World Civilization, World History</p></td></tr><tr><td><p>Special Education: Deaf and Hard of Hearing, Early Childhood Special Education, Gifted and Talented Education, Hearing Impaired, High Ability Educations, Intense Intervention, Learning Disabled, Mild Intervention, Mildly Mentally Handicapped, Physically Handicapped, Seriously Emotionally Handicapped</p></td></tr><tr><td><p>Other: Coaching, Early Childhood, Elementary/Intermediate Generalist, Home Economics, Kindergarten, World Languages (French, German, etc.), Health and Safety, Intrumental and General Music, Library/Media Services, Music, Physical Education, School Counselor, School Psychologist, Substitute Permit</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0157152040-8">Results</hd> <p></p> <hd id="AN0157152040-9">What Licenses Do Indiana CS Teachers Have Who Are Teaching CS Courses?</hd> <p>Teacher's licensure status was defined by grouping teachers into one of three mutually exclusive categories: CS-related licenses, approved licenses, or not approved licenses. Teachers who had one or more CS or CS-Related licenses (n = 203) were identified as having a "CS-Related" licensure status (Table 1). Teachers who had one or more licenses that were approved for teaching CS courses but that are not directly related to CS (n = 196) were identified as having an "Approved" licensure status (e.g., Math, Science, or Business). Teachers who did not have any license that fell into either category (n = 110) were identified as having a "Not Approved" licensure status.</p> <p>The research team was unable to obtain school-level data for all private schools, all Career Center schools, and one public school. Because the subsequent research question relies on this school-level data, teachers were dropped from our sample when this data was missing. This resulted in a sample of 416 teachers in 303 public schools. This affected the analysis as teacher licensure status was highly correlated with the type of school in which they were employed. Thus, to better contextualize the analyses for the second research question, we implemented a cross-tabulation of teacher licensure status and school type (see Fig. 1).</p> <p>Graph: Fig. 1 CS teacher licenses by school type</p> <hd id="AN0157152040-10">What Is the Relationship between the Demographics of a School and CS Teachers' Licenses?</hd> <p>To investigate the relationship between the demographics of a school and CS teachers' licenses, we aggregated the data to school level. If the school had more than one CS teacher, we defaulted to the highest level of qualified teacher license (starting with CS-Related, then Approved, then Not Approved). Schools with one or more teachers that have a CS-Related license were categorized as "CS-Related"; schools with one or more teachers that have an approved license but no teacher with a CS-Related license were categorized as "Approved"; and schools without any teacher with an approved or CS-Related license were categorized as "Not Approved." Therefore, if a school had one teacher who had a CS-Related license and one teacher who had a license that was not approved, we counted that school as "CS-Related." The rationale for this was that schools may be using this opportunity to co-teach or mentor more novice CS teachers who may currently be teaching under an emergency permit and/or preparing to earn a CS-Related license.</p> <p>Our original plan was to conduct multinomial logistic regression analysis with licensure status as the dependent variable. However, only a small percentage of schools in our sample (8%) fell into the "Not Approved" category (i.e., they had no CS teachers with either an approved or CS-Related license). Preliminary analyses suggested that this number was insufficient for making meaningful comparisons with the other two license categories, especially when controlling for multiple demographic covariates. Therefore, we determined it would be more meaningful to compare schools with and without one or more teachers who had a CS-Related license, and we created a binary outcome variable to reflect this dichotomy. Next we conducted logistic regression analysis with predictor variables that included school locale, percent of students qualifying for free or reduced-price lunch, and percent of students by distinct race/ethnicity categories.</p> <hd id="AN0157152040-11">What Is the Relationship between Locale (Suburban, Urban, Rural, and Town) and CS Teacher Lic...</hd> <p>To aid in specifying the model, we examined descriptive statistics for these variables, reported below. Slightly higher percentages of city and suburban schools (59% and 56%, respectively) had one or more teachers with a CS-Related license than town and rural schools (48% and 46%, respectively; see Fig. 2). This is further evidenced in Fig. 3, which showcases the relationship between teachers with CS-related licenses and counties with low population density (rural and town) versus high population density (city and suburban) (see Fig. 3).</p> <p>Graph: Fig. 2 Percent of schools with without a teacher with a CS-related license by locale</p> <p>Graph: Fig. 3 Relationship between the number of teachers with CS-related licenses and population density</p> <hd id="AN0157152040-12">Relationship between Students' Race/Ethnicity, Free or Reduced Price Lunch Status, and CS Tea...</hd> <p>​​ For each demographic category, there was little variation in mean percentages between schools with and without a teacher with a CS-Related license (see Table 2). However, after disaggregating this data across locale categories, some larger differences emerged between schools of differing teacher license categories, particularly in terms of the percentage of Black students and White students (see Table 3). We thus determined to include an interaction term in the model between locale and race/ethnicity.</p> <p>Table 2 Mean Percent of students at Schools with and without a teacher with a CS-related license</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th rowspan="2"><p>Demographic category</p></th><th colspan="2"><p>CS-related license</p></th></tr><tr><th><p>No</p></th><th><p>Yes</p></th></tr></thead><tbody><tr><td><p>American Indian or Alaska Native</p></td><td><p>0.17%</p></td><td><p>0.22%</p></td></tr><tr><td><p>Asian (2% of IN student population)</p></td><td><p>1.49%</p></td><td><p>1.87%</p></td></tr><tr><td><p>Black (8% of IN student population)</p></td><td><p>7.79%</p></td><td><p>9.41%</p></td></tr><tr><td><p>Hispanic or Latino</p></td><td><p>9.36%</p></td><td><p>11.77%</p></td></tr><tr><td><p>Two or more races</p></td><td><p>3.58</p></td><td><p>4.11%</p></td></tr><tr><td><p>Native Hawaiian or Other Pacific Islander</p></td><td><p>0.10%</p></td><td><p>0.06%</p></td></tr><tr><td><p>White</p></td><td><p>77.52%</p></td><td><p>72.56%</p></td></tr></tbody></table> </ephtml> </p> <p>Table 3 Mean demographic proportions by locale for schools with (CS) and without (NonCS) a CS-related license</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th rowspan="2"><p>Demographic category</p></th><th colspan="2"><p>City</p></th><th colspan="2"><p>Suburban</p></th><th colspan="2"><p>Town</p></th><th colspan="2"><p>Rural</p></th></tr><tr><th><p>NonCS</p></th><th><p>CS</p></th><th><p>NonCS</p></th><th><p>CS</p></th><th><p>NonCS</p></th><th><p>CS</p></th><th><p>NonCS</p></th><th><p>CS</p></th></tr></thead><tbody><tr><td><p>Free or Reduced-Price Lunch</p></td><td><p>.59</p></td><td><p>.55</p></td><td><p>.34</p></td><td><p>.45</p></td><td><p>.44</p></td><td><p>.42</p></td><td><p>.39</p></td><td><p>.38</p></td></tr><tr><td><p>American Indian/Alaska Native</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td></tr><tr><td><p>Asian</p></td><td><p>.03</p></td><td><p>.04</p></td><td><p>.02</p></td><td><p>.02</p></td><td><p>.01</p></td><td><p>.01</p></td><td><p>.01</p></td><td><p>.01</p></td></tr><tr><td><p>Black</p></td><td><p>.34</p></td><td><p>.19</p></td><td><p>.07</p></td><td><p>.18</p></td><td><p>.01</p></td><td><p>.02</p></td><td><p>.01</p></td><td><p>.02</p></td></tr><tr><td><p>Hispanic/Latino</p></td><td><p>.14</p></td><td><p>.19</p></td><td><p>.15</p></td><td><p>.19</p></td><td><p>.09</p></td><td><p>.06</p></td><td><p>.06</p></td><td><p>.05</p></td></tr><tr><td><p>Two or more races</p></td><td><p>.07</p></td><td><p>.06</p></td><td><p>.04</p></td><td><p>.05</p></td><td><p>.03</p></td><td><p>.03</p></td><td><p>.02</p></td><td><p>.03</p></td></tr><tr><td><p>Native Hawaiian/Pacific Islander</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td><td><p>.00</p></td></tr><tr><td><p>White</p></td><td><p>.42</p></td><td><p>.52</p></td><td><p>.71</p></td><td><p>.55</p></td><td><p>.86</p></td><td><p>.88</p></td><td><p>.90</p></td><td><p>.89</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0157152040-13">Logistic Regression Analysis of CS License</hd> <p>We conducted preliminary analyses to ensure that the data and models were appropriate for the intended analysis. No patterns of missing data were evident. We were interested in looking at the impact of an underrepresented population's composition, so we combined the percentages of Black, Hispanic, American Indian/Alaska Native, Native Hawaiian/Pacific Islander, or of two or more races, and made a percentage of minority ethnic groups.</p> <p>To examine the relationship between CS licenses and attributes of a school, we formulated a logistic regression model. The model predicts the probability of one school having at least one teacher with a CS license by a set of variables of interest including a school's locale, free/reduced lunch rate, percentage of minority ethnic groups.</p> <p>The interactions between the variables were investigated by comparing the nested model only with the main effects and the full model with the interaction terms. The interactions between the locale and ethnic composition did not make a significantly larger contribution in explaining the variations of the binary response, which was indicated by the likelihood ratio test (<emph>LRT</emph> = 4.1506, <emph>df</emph> = 3, <emph>p</emph> =.25). Therefore, we chose a simpler model and its coefficients are summarized in Table 4. None of the variables were found to be significant from the logistic regression model. This means that we did not observe differences of CS teacher license in terms of a school's locale, free/reduced lunch rate, and ethnic compositions.</p> <p>Table 4 Coefficients of the logistic regression model</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th /><th><p><italic>β</italic></p></th><th><p>SE(<italic>β</italic>)</p></th><th><p><italic>Z</italic></p></th><th><p><italic>p</italic></p></th></tr></thead><tbody><tr><td><p>Intercept</p></td><td><p>.28</p></td><td><p>.46</p></td><td><p>.62</p></td><td><p>.54</p></td></tr><tr><td char="." align="char" colspan="5"><p>Locale*</p></td></tr><tr><td><p> Rural</p></td><td><p>.08</p></td><td><p>.36</p></td><td><p>.21</p></td><td><p>.83</p></td></tr><tr><td><p> Suburb</p></td><td><p>.09</p></td><td><p>.34</p></td><td><p>.24</p></td><td><p>.81</p></td></tr><tr><td><p> Town</p></td><td><p>.03</p></td><td><p>.40</p></td><td><p>.07</p></td><td><p>.94</p></td></tr><tr><td><p>FRL Percentage</p></td><td><p>.00</p></td><td><p>.93</p></td><td><p>.00</p></td><td><p>.10</p></td></tr><tr><td><p>Underrepresented Minority Percentage</p></td><td><p>−.43</p></td><td><p>.75</p></td><td><p>−.57</p></td><td><p>.57</p></td></tr></tbody></table> </ephtml> </p> <p>*<emph>Note</emph>. Locale contains City, Rural, Suburb, and Town, and in this model/locale was referenced by City</p> <hd id="AN0157152040-14">Discussion</hd> <p>Studies have shown the importance of certifying CS teachers. In fact, Warner et al. ([<reflink idref="bib44" id="ref34">44</reflink>]) found a negative relationship between the proportion of teachers who are CS-certified and the proportion of underrepresented minority students enrolled across 13 CS courses in Texas. In addition, studies have also shown the importance of teacher certification, showing positive relationships between teacher certification and student achievement (Betts et al., [<reflink idref="bib1" id="ref35">1</reflink>]; Fetler, [<reflink idref="bib13" id="ref36">13</reflink>]; Goe, [<reflink idref="bib20" id="ref37">20</reflink>]). In other words, certified teachers tend to show higher student achievement and test scores.</p> <p>One of the positive outcomes of this study showed that 85% of current public school teachers who teach CS have a CS-Related or Approved license. As of 2018, Indiana legislation requires high schools to offer a CS course at least once per year by July 2021 (S. E. A. 172, [<reflink idref="bib35" id="ref38">35</reflink>]). Fewer than 20% of all Indiana high schools offered CS in 2014-2015, whereas 66% of all Indiana high schools offered CS in 2020-2021 (Indiana Department of Education, [<reflink idref="bib26" id="ref39">26</reflink>]). This dramatic change in offerings required many schools to add new teachers qualified to teach CS or determine which existing staff would teach CS. Therefore, with the uptake in schools offering, 85% of current public school CS teachers in Indiana having relevant certification is a feat to be celebrated.</p> <p>In general, typically lower income schools and those with higher ratios of students of color tend to have a lower percentage of highly qualified teachers (Darling-Hammond, [<reflink idref="bib8" id="ref40">8</reflink>]). According to this Indiana data, there were no significant differences between CS-related teacher licenses and the schools' ethnic compositions. Concerns on the lack of different underrepresented populations (e.g., female, Hispanic, Black, economically disadvantaged, English-language learners, etc.) in CS education for K-12 have been documented by different organizations (Code.org et al., [<reflink idref="bib7" id="ref41">7</reflink>]; Gallup, [<reflink idref="bib18" id="ref42">18</reflink>], [<reflink idref="bib19" id="ref43">19</reflink>]). Studies have shown that having CS teachers who belong to these underrepresented populations is impactful (Scott et al., [<reflink idref="bib37" id="ref44">37</reflink>]; Seneviratne, [<reflink idref="bib38" id="ref45">38</reflink>]). These teachers serve as role models and help students who belong to the same underrepresented population see that CS is an option for them.</p> <p>The analysis of this study also showed no significant differences with economically disadvantaged students (free and reduced lunch status). In Texas, Warner et al. ([<reflink idref="bib44" id="ref46">44</reflink>]) found that fewer than 40% of Title 1 urban/suburban schools (which have a higher percentages of economically disadvantaged students) had a CS-certified teacher, where as 55% of non-Title urban/suburban schools had a CS-certified teacher. These numbers dropped even further with less than 20% of Title 1 rural schools having a CS-certified teacher (Warner et al., [<reflink idref="bib44" id="ref47">44</reflink>]). This difference between Indiana and others across the country is a positive indication that outreach and professional development efforts in Indiana have positively impacted schools' abilities to offer computer science regardless of the dominant student economic background within the school.</p> <p>This could be due, in part, to how the state has approached CS education. Indiana has a state K-12 CS education plan. Within this plan, Indiana has focused on teacher capacity through targeted professional development funding and programs. In addition, Indiana collects data on CS offerings, enrollments, and teachers, which assists in their ability to identify the schools that have not been able to offer CS. Once a school has been identified as not offering CS, Department of Education staff reach out and work with the school to identify necessary supports. Oftentimes this is by offering various professional development and certification preparation opportunities through curriculum (e.g., Nextech, CodeHS, and Project Lead The Way) and university partners. Through these providers, schools and teachers can select which course options and sequences best fits their needs. With annual funding from the Indiana General Assembly beginning in 2019, teacher professional development opportunities have been available to schools free of charge, alleviating the cost prohibitive barriers sometimes associated with teacher training and licensing (H.E.A. 1001, [<reflink idref="bib21" id="ref48">21</reflink>]; [<reflink idref="bib22" id="ref49">22</reflink>]).</p> <p>Within the state of Indiana, 79% of all private and public CS teachers had a CS-Related license or an Approved license. With the quick addition of CS into K-12 education, this amount of teachers with appropriate licenses is impressive. However, this also means that 21% of teachers currently teaching CS do not have a CS-Related or approved license.</p> <p>Our results showed that there were no systematic differences between schools' student demographics (both race and FRL status) and the likelihood of having a CS teacher with a CS-approved license. In fact, the descriptive statistics seem to follow the population density of the state. This suggests that CS-approved teachers are distributed relatively evenly throughout the state, leading to more equitable availability of teachers with CS licensing to underrepresented groups of students. This finding could suggest that current policies and practices the Indiana Department of Education has put in place to ensure knowledgeable CS teachers are in place across the state is working. However, there are two areas where we found differences (private versus public, and rural/town versus urban/suburban). We discuss these in greater detail below.</p> <hd id="AN0157152040-15">Private Schools Versus Public Schools</hd> <p>Although 85% of all public school CS teachers had an approved license for teaching CS, private schools were less likely to have teachers who had a CS-Related or Approved license. In fact, only 44% of private schools had teachers with an approved license to teach CS. However, within Indiana, private schools are under different regulations. In fact, private schools can decide on what qualifications their teachers need to have. The only exception is that if they participate in the Choice Scholarship (voucher) Program, they have state accreditation (or another accreditation agency) which requires that they hire licensed teachers to teach certain subjects. Additionally, the requirement for Indiana high schools to offer a CS course does not technically apply to non-public schools.</p> <p>Since private schools do not have to follow the same type of licensing rules that public schools do, it is possible that private school teachers have alternative qualifications that enable them to teach CS without the licensing from the state. This could include teachers coming from CS-related jobs that never went through the licensing process as private school teachers do not necessarily need to have a teaching license to teach in the school. The 12.9% of public schools that have teachers teaching CS courses without an approved license would need to be investigated further in order to understand the circumstances that led a non-approved CS teacher to teach a CS course.</p> <p>Forty percent of all CS teachers had a CS-Related license. The CS-Related licenses ensure that teachers have specified CS knowledge. The Approved licenses included Business, Math, Science, and Technology Education. In the cases of Math, Science, and Technology Education licenses, teachers are approved if they participate in professional development or additional training in computer science (Indiana Department of Education, [<reflink idref="bib27" id="ref50">27</reflink>]). This is something that is tracked and approved at the local level by school/district administration. While many teachers with these approved licenses participate in curriculum-focused professional development that is aligned to the course(s) they are teaching, there is no guarantee that the professional development experience covers all content and pedagogical knowledge that would equate to the knowledge held by a CS-Related licensed teacher. Furthermore, it is difficult to ensure consistency and adequacy when professional development and training is tracked locally.</p> <p>In the case of the Business licenses, this was a historically approved license that dates back to the 1980s. It is unclear whether the teacher preparation requirements to obtain this license include computer science content. In fact, it is likely that Business content covered in the 1980s included little CS knowledge relevant to today's standards. The one mention of computer science in the Business educator standards states "9.10 knowledge and skills of computer programming, including an understanding of various programming languages" (Indiana Department of Education, [<reflink idref="bib24" id="ref51">24</reflink>], p. 7). This license may have been traditionally included as approved for computer science because of a connection between business and information technology (often conflated with CS).</p> <hd id="AN0157152040-16">Rural Versus Urban and Suburban Schools</hd> <p>Within Indiana, the Town and Rural schools have a smaller population density while City and Suburban schools have a much larger population density. Though we did not find considerably large differences from the inferential model, we did observe some gaps between the schools in urban/suburban and those in rural/town. Town and rural schools were less likely to have one or more teachers with a CS-related license (48% and 46%, respectively; see Fig. 1). While slightly higher percentages of city and suburban schools (59% and 56%, respectively) had one or more teachers with a CS-Related license.</p> <p>For the rural and town schools, the most common licenses for those teaching CS were business (36%) and mathematics (10%). Teachers of CS in rural and town schools had over 50 different types of licenses. These findings could be related to historical teacher retention issues in rural school settings where schools sometimes rely on emergency licensing for teachers in order to teach subject areas where an appropriately licensed teacher cannot be identified. Also, since Business licenses have a historical context, as Business licenses (originally called "Commerce") were awarded beginning in 1923 (R. Regnier, personal communication, December 16, 2021). As math is a required subject for all high schools, and a graduation requirement, it makes sense that rural and town schools would have math teachers and would leverage those teachers to teach CS.</p> <p>This could be due, in part, to the fact that smaller and less densely populated school districts have a difficult time obtaining qualified teachers. Studies have shown that rural and town schools have lower STEM teaching capacity and thus also offered limited access to advanced coursework and extracurricular programs in STEM (Saw & Agger, [<reflink idref="bib36" id="ref52">36</reflink>]). Within rural schools, teachers were less likely to participate in PD focused on how students learn in STEM, and those schools had fewer STEM teachers. In addition, rural and town schools were less likely to have after school STEM activities (like science fairs, competitions, and afterschool clubs). Saw and Agger ([<reflink idref="bib36" id="ref53">36</reflink>]) argued that this lack of opportunities to learn STEM likely contributed to fewer rural and town students pursuing STEM postsecondary programs. In other words, the lack of opportunities to learn in STEM are widening geographic gaps in STEM academic preparation.</p> <hd id="AN0157152040-17">Implications</hd> <p>The implications of this study center around the need for structures and programs that adequately prepare both pre-service and in-service teachers to teach CS. Although 85% of Indiana public school teachers that taught CS had a CS-Related or Approved license, we also recognized that this only includes information on the 354 (66%) Indiana schools that offer CS. There are still an additional 186 (34%) Indiana high schools that did not offer CS during the 2020-2021 school year. Since Indiana has mandated that ALL public and public charter high schools must offer at least one high school CS course beginning with the 2021-2022 school year, many of these schools likely need to procure a teacher to teach CS. Therefore, there is going to continue to be a need for more teachers with licenses to teach CS. With increased attention on expanding CS education globally, the need for qualified CS teachers is not unique to Indiana.</p> <p>Considerations should be made for strategies to ensure that adequately prepared and licensed CS teachers are available to all schools regardless of the demographic makeup of the students or the locale of the school. Professional development for in-service teachers is a strategy to address this, however funding for such efforts provided by state and national education agencies may not exist in perpetuity. As such, a two-pronged approach involving training a critical mass of existing teachers to teach CS while also developing sustainable teacher preparation models will be essential to long-term success. This indicates there may be a need for policies or legislation to be enacted that increase the availability of CS teacher preparation programs and/or incentivize teachers to become licensed in CS and teach CS in schools.</p> <p>Preservice programs provide one strategy for producing a sustainable supply of future computer science teachers. Because there are very few preservice programs that specifically aim to prepare computer science teachers, policies could be developed that provide teacher preparation programs and schools of education with funds to plan and implement such programs (DeLyser et al., [<reflink idref="bib9" id="ref54">9</reflink>]). Once adequate preparation programs are in place, additional funds may be needed for student recruitment and marketing.</p> <p>Achieving the ability for all high schools to be able to offer computer science courses through a licensed computer science teacher is one of the first steps to increasing K-12 CS education access (Code et al., [<reflink idref="bib7" id="ref55">7</reflink>]). Indiana and other states and countries that reach this achievement will be able to focus more on increasing enrollment and course options and, ultimately, focus on creating high-quality computer science experiences for students in computer science classes. While knowledgeable and well-trained teachers are the key to making computer science implementable by all schools and accessible to all students, future research needs to focus on best practices that can aid in ensuring that the number of students pursuing computer science coursework continues to increase. Ideally, this will lead to student CS enrollment demographics mirroring those of a given student body overall.</p> <hd id="AN0157152040-18">Declarations</hd> <p></p> <hd id="AN0157152040-19">Conflicts of Interests/Competing Interests</hd> <p>The authors have no conflicts or competing interests to declare that are relevant to the content of this article.</p> <hd id="AN0157152040-20">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0157152040-21"> <title> References </title> <blist> <bibl id="bib1" idref="ref35" type="bt">1</bibl> <bibtext> Betts JR, Reuben KS, Danenberg A. Equal resources, equal outcomes? The distribution of school resources and student achievement in California. 2000; Public Policy Institute of California</bibtext> </blist> <blist> <bibl id="bib2" idref="ref6" type="bt">2</bibl> <bibtext> Blikstein, P, & Moghadam, S. H. (2019). Computing education: Literature review and voices from the field. The Cambridge handbook of computing education research. 56-78. https://doi.org/10.1017/9781108654555.004.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref16" type="bt">3</bibl> <bibtext> California Department of Education. (2021). Fingertip facts on education in California.https://<ulink href="http://www.google.com/url?q=https://www.cde.ca.gov/ds/ad/ceffingertipfacts.asp&sa=D&source=docs&ust=1645475783870557&usg=AOvVaw3HLfui8zQJ%5fVXexJ%5fZTVKK">www.google.com/url?q=https://www.cde.ca.gov/ds/ad/ceffingertipfacts.asp&sa=D&source=docs&ust=1645475783870557&usg=AOvVaw3HLfui8zQJ%5fVXexJ%5fZTVKK</ulink>.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref7" type="bt">4</bibl> <bibtext> CEDEFOP. The great divide: Digitalization and digital skill gaps in the EU workforce. ESJsurvey Insights. 2016; 9; 9: 1-16Retrieved from https://<ulink href="http://www.cedefop.europa.eu/files/esj%5finsight%5f9%5fdigital%5fskills%5ffinal.pdf">www.cedefop.europa.eu/files/esj%5finsight%5f9%5fdigital%5fskills%5ffinal.pdf</ulink></bibtext> </blist> <blist> <bibl id="bib5" idref="ref1" type="bt">5</bibl> <bibtext> Code.org, CSTA, & ECEP Alliance. (2019). 2019 state of computer science education: Equity and diversity. https://advocacy.code.org/2019_state_of_cs.pdf</bibtext> </blist> <blist> <bibl id="bib6" idref="ref2" type="bt">6</bibl> <bibtext> Code.org, CSTA, & ECEP Alliance. (2020). 2020 state of computer science education: Illuminating disparities. https://advocacy.code.org/2020_state_of_cs.pdf</bibtext> </blist> <blist> <bibl id="bib7" idref="ref3" type="bt">7</bibl> <bibtext> Code.org, CSTA, & ECEP Alliance. (2021). 2021 state of computer science education: Accelerating action through advocacy. https://advocacy.code.org/2021_state_of_cs.pdf</bibtext> </blist> <blist> <bibl id="bib8" idref="ref40" type="bt">8</bibl> <bibtext> Darling-Hammond L. Inequality and the right to learn: Access to qualified teachers in California. Teachers College Record. 2004; 106; 10: 1936-1966. 10.1111/j.1467-9620.2004.00422.x</bibtext> </blist> <blist> <bibl id="bib9" idref="ref54" type="bt">9</bibl> <bibtext> Delyser L, Goode J, Guzdial M, Kafai Y, Yadav A. Priming the computer science teacher pump: Integrating computer science education into schools of education. 2018; CSforALL</bibtext> </blist> <blist> <bibtext> Delyser, L. A. (2016). Building a computer science teacher pipeline for New York city: A gathering of teacher preparation programs. New York City Foundation for Computer Science Education. Retrieved from https://<ulink href="http://www.csfored.org/report2016">www.csfored.org/report2016</ulink>.</bibtext> </blist> <blist> <bibtext> European Commission/EACEA/Eurydice. Digital education at school in Europe. Eurydice report. 2019; Publications Office of the European UnionRetrieved from https://eacea.ec.europa.eu/national-policies/eurydice/sites/default/files/en_digital_education_n.pdf</bibtext> </blist> <blist> <bibtext> Falkner, F, Sentance, S, Vivian, R, Barksdale, S, Busuttil, L, Cole, E, Liebe, C, Maiorana, F, McGill, M, & Quille, K. (2019). An international comparison of K-12 computer science education intended and enacted curricula. In Proceedings of the 19th Koli Calling International Conference on Computing Education Research (Koli Calling '19) (pp. 1-10). Association for Computing Machinery. https://doi.org/10.1145/3364510.3364517.</bibtext> </blist> <blist> <bibtext> Fetler, M. (1999). High school staff characteristics and mathematics test results. Education Policy Analysis Archives, 7(9).</bibtext> </blist> <blist> <bibtext> Fletcher CL, Warner JR. CAPE: A framework for assessing equity throughout the computer science education ecosystem. Communications of the ACM. 2021; 64; 2: 23-25. 10.1145/3442373</bibtext> </blist> <blist> <bibtext> Fuhrman SH. Designing coherent education policy: Improving the system. 1993; Josey-Bass Publishers</bibtext> </blist> <blist> <bibtext> G20. (2018). G20 education ministers' declaration 2018: Building consensus for fair and sustainable development.</bibtext> </blist> <blist> <bibtext> Gal-Ezer J, Stephenson C. A tale of two countries: Successes and challenges in K-12 computer science education in Israel and the United States. ACM Transactions on Computing Education. 2014; 14; 2: 1-18. 10.1145/2602483</bibtext> </blist> <blist> <bibtext> Gallup, Inc. (2020a). Moving forward: closing the computer science learning gap -- black students. https://csedu.gallup.com/home.aspx.</bibtext> </blist> <blist> <bibtext> Gallup, Inc. (2020b). Moving forward: closing the computer science learning gap -- girls. https://csedu.gallup.com/home.aspx.</bibtext> </blist> <blist> <bibtext> Goe L. The distribution of emergency permit teachers in California. Education Policy Analysis Archives. 2002; 10: 42. 10.14507/epaa.v10n42.2002</bibtext> </blist> <blist> <bibtext> H. E. A. 1001, 2019 Biennium, 2019 Reg. Sess. (IN 2019). <ulink href="http://iga.in.gov/legislative/2019/bills/house/1001#document-a47201d3">http://iga.in.gov/legislative/2019/bills/house/1001#document-a47201d3</ulink></bibtext> </blist> <blist> <bibtext> H. E. A. 1001, 2021 Biennium, 2021 Reg. Sess. (IN 2021). <ulink href="http://iga.in.gov/legislative/2021/bills/house/1001#document-dbc2cc8e">http://iga.in.gov/legislative/2021/bills/house/1001#document-dbc2cc8e</ulink></bibtext> </blist> <blist> <bibtext> Han, D, & Hur, H. (2021). Managing turnover of STEM teacher workforce. Education and Urban Society, 1–18. https://doi.org/10.1177/00131245211053562.</bibtext> </blist> <blist> <bibtext> Indiana Department of Education. (2010). Indiana Content Standards for Educators. Business Education. https://<ulink href="http://www.in.gov/doe/files/inedstandards-business.pdf">www.in.gov/doe/files/inedstandards-business.pdf</ulink>.</bibtext> </blist> <blist> <bibtext> Indiana Department of Education. (2021a). IC 20-20-45 next level computer science program biannual report. https://<ulink href="http://www.in.gov/doe/files/SEA-172-2018-Gov-CS-Report-July-2021-with-Appendices.pdf">www.in.gov/doe/files/SEA-172-2018-Gov-CS-Report-July-2021-with-Appendices.pdf</ulink>.</bibtext> </blist> <blist> <bibtext> Indiana Department of Education. (2021b). SEA 295 (2020), amendment to IC 20-30-5-23 - computer science (CS) report.https://<ulink href="http://www.in.gov/doe/files/SEA-295-2020-Report-December-1,-2021.pdf">www.in.gov/doe/files/SEA-295-2020-Report-December-1,-2021.pdf</ulink>.</bibtext> </blist> <blist> <bibtext> Indiana Department of Education (n.d.). What can I teach with my Indiana license?https://<ulink href="http://www.in.gov/doe/educators/educator-licensing/what-can-i-teach-with-my-license/">www.in.gov/doe/educators/educator-licensing/what-can-i-teach-with-my-license/</ulink></bibtext> </blist> <blist> <bibtext> Kim, J, Edwards, M. K, Childs, J, Fletcher, L. C, Leftwich, A, & Hendrickson, K. (2021). The landscape of computer science teacher qualification pathway [Poster. presentation]. SIGCSE Technical Symposium, Online.</bibtext> </blist> <blist> <bibtext> Neethipudi, B, Fares, K, Fowler, B, & Vegas, E. (2021). How South Korea implemented its computer science education program. Retrieved from https://<ulink href="http://www.brookings.edu/wp-content/uploads/2021/10/How-S-Korea-implemented-its-CS-program%5fFINAL.pdf">www.brookings.edu/wp-content/uploads/2021/10/How-S-Korea-implemented-its-CS-program%5fFINAL.pdf</ulink>.</bibtext> </blist> <blist> <bibtext> Ni, L, Mcklin, T, Hao, H, Baskin, J, Bohrer, J, & Tian, Y. (2021). Understanding professional identity of computer science teachers: Design of the computer science teacher identity survey. ICER 2021 - Proceedings of the 17th ACM Conference on International Computing Education Research, 281–293. https://doi.org/10.1145/3446871.3469766.</bibtext> </blist> <blist> <bibtext> Nouri J, Zhang L, Mannila L, Norén E. Development of computational thinking, digital competence and 21st century skills when learning programming in K-9. Education Inquiry. 2020; 11; 1: 1-17. 10.1080/20004508.2019.1627844</bibtext> </blist> <blist> <bibtext> Ottenbreit-Leftwich, A, Childs, J, Fletcher, C, Kim, J, Monteros, K, & Hendrickson, K. (in press). Defining computer science teacher qualification pathways. Policy Futures in Education.</bibtext> </blist> <blist> <bibtext> Ottenbreit-Leftwich, A, DeLyser, L, Brush, T, Childs, J, Kim, J, Koressel, J, Lee, H, Chu, L, Yadav, A, & Simon, B. (2021). Status of US national preservice CS teacher education.</bibtext> </blist> <blist> <bibtext> Pennington McVey, K, Trinidad, J, & Partners, B. E. (2019). Nuance in the noise: The complex reality of teacher shortages. Bellweather education Partners, (January). Retrieved from. <ulink href="http://ezproxy.lib.ucalgary.ca/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=ED596444&site=ehost-live">http://ezproxy.lib.ucalgary.ca/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=ED596444&site=ehost-live</ulink></bibtext> </blist> <blist> <bibtext> S. E. A. 172, 2017 Biennium, 2018 Reg. Sess. (IN 2018). <ulink href="http://iga.in.gov/legislative/2018/bills/senate/172#document-24bd1fac">http://iga.in.gov/legislative/2018/bills/senate/172#document-24bd1fac</ulink></bibtext> </blist> <blist> <bibtext> Saw, G. K, & Agger, C. A. (2021). STEM pathways of rural and small-town students: Opportunities to learn, aspirations, preparation, and college enrollment. Educational Researcher.https://doi-org.proxyiub.uits.iu.edu/10.3102/0013189X211027528</bibtext> </blist> <blist> <bibtext> Scott, A, Martin, A, & McAlear, F. (2017). Enhancing participation in computer science among girls of color: An examination of a preparatory AP computer science intervention. In moving students of color from consumers to producers of technology (pp. 62-84). IGI Global.</bibtext> </blist> <blist> <bibtext> Seneviratne, O. (2017). Making computer science attractive to high school girls with computational thinking approaches: A case study. In Emerging research, practice, and policy on computational thinking (pp. 21-32). Springer.</bibtext> </blist> <blist> <bibtext> Texas Education Agency. (2020). Enrollment in Texas public schools 2019-20.https://<ulink href="http://www.google.com/url?q=https://tea.texas.gov/sites/default/files/enroll%5f2019-20.pdf&sa=D&source=docs&ust=1645475715647429&usg=AOvVaw0fRDVmscmOyrwms%5fPdaGlB">www.google.com/url?q=https://tea.texas.gov/sites/default/files/enroll%5f2019-20.pdf&sa=D&source=docs&ust=1645475715647429&usg=AOvVaw0fRDVmscmOyrwms%5fPdaGlB</ulink>.</bibtext> </blist> <blist> <bibtext> The White House. (2012). 100Kin10: Answering the nation's call. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/cahill_pcast.pdf.</bibtext> </blist> <blist> <bibtext> Tissenbaum M, Ottenbreit-Leftwich A. A vision of K-12 computer science education for 2030. Communications of the ACM. 2020; 63; 5: 42-44. 10.1145/3386910</bibtext> </blist> <blist> <bibtext> United States Bureau of Labor Statistics. (2021). Occupational Outlook Handbook.https://<ulink href="http://www.bls.gov/ooh/">www.bls.gov/ooh/</ulink>.</bibtext> </blist> <blist> <bibtext> Vahrenhold, J, Caspersen, M, Berry, G, Gal-Ezer, J, Kölling, M, McGettrick, A, Nardelli, E, Pereira, C, & Westermeier, M. (2017). Informatics education in Europe: Are we all in the same boat? ACM and Informatics Europe.<ulink href="http://www.informatics-europe.org/component/phocadownload/category/10-reports.html?download=60:cece-report">http://www.informatics-europe.org/component/phocadownload/category/10-reports.html?download=60:cece-report</ulink></bibtext> </blist> <blist> <bibtext> Warner, J. R, Fletcher, C. L, Torbey, R, Garbrecht, L. S. (2019). Scaling certification incentives and professional development to increase equitable access to qualified computer science teachers. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 1157-1163). Retrieved from https://par.nsf.gov/servlets/purl/10119284</bibtext> </blist> <blist> <bibtext> Zarch, R, Dunton, S, Childs, J, & Leftwich, A. (2020). Through the looking glass: Computer science education and the unintended consequences of broadening participation policy efforts. In 2020 research on equity and sustained participation in engineering, computing, and technology (RESPECT) (Vol. 1, pp. 1-4). IEEE. https://doi.org/10.1109/RESPECT49803.2020.9272480.</bibtext> </blist> </ref> <aug> <p>By Jacob Koressel; Anne Ottenbreit-Leftwich; Katie Jantaraweragul; Minji Jeon; Jayce Warner and Matthew Brown</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib11" firstref="ref4"></nolink> <nolink nlid="nl2" bibid="bib31" firstref="ref5"></nolink> <nolink nlid="nl3" bibid="bib16" firstref="ref8"></nolink> <nolink nlid="nl4" bibid="bib41" firstref="ref9"></nolink> <nolink nlid="nl5" bibid="bib42" firstref="ref10"></nolink> <nolink nlid="nl6" bibid="bib29" firstref="ref12"></nolink> <nolink nlid="nl7" bibid="bib15" firstref="ref14"></nolink> <nolink nlid="nl8" bibid="bib39" firstref="ref15"></nolink> <nolink nlid="nl9" bibid="bib17" firstref="ref17"></nolink> <nolink nlid="nl10" bibid="bib32" firstref="ref18"></nolink> <nolink nlid="nl11" bibid="bib35" firstref="ref19"></nolink> <nolink nlid="nl12" bibid="bib26" firstref="ref20"></nolink> <nolink nlid="nl13" bibid="bib12" firstref="ref21"></nolink> <nolink nlid="nl14" bibid="bib43" firstref="ref22"></nolink> <nolink nlid="nl15" bibid="bib14" firstref="ref24"></nolink> <nolink nlid="nl16" bibid="bib30" firstref="ref25"></nolink> <nolink nlid="nl17" bibid="bib23" firstref="ref26"></nolink> <nolink nlid="nl18" bibid="bib34" firstref="ref27"></nolink> <nolink nlid="nl19" bibid="bib40" firstref="ref28"></nolink> <nolink nlid="nl20" bibid="bib33" firstref="ref29"></nolink> <nolink nlid="nl21" bibid="bib10" firstref="ref30"></nolink> <nolink nlid="nl22" bibid="bib28" firstref="ref31"></nolink> <nolink nlid="nl23" bibid="bib45" firstref="ref32"></nolink> <nolink nlid="nl24" bibid="bib44" firstref="ref34"></nolink> <nolink nlid="nl25" bibid="bib13" firstref="ref36"></nolink> <nolink nlid="nl26" bibid="bib20" firstref="ref37"></nolink> <nolink nlid="nl27" bibid="bib18" firstref="ref42"></nolink> <nolink nlid="nl28" bibid="bib19" firstref="ref43"></nolink> <nolink nlid="nl29" bibid="bib37" firstref="ref44"></nolink> <nolink nlid="nl30" bibid="bib38" firstref="ref45"></nolink> <nolink nlid="nl31" bibid="bib21" firstref="ref48"></nolink> <nolink nlid="nl32" bibid="bib22" firstref="ref49"></nolink> <nolink nlid="nl33" bibid="bib27" firstref="ref50"></nolink> <nolink nlid="nl34" bibid="bib24" firstref="ref51"></nolink> <nolink nlid="nl35" bibid="bib36" firstref="ref52"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1336973
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Investigating CS Teacher Licensure in Indiana
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Koressel%2C+Jacob%22">Koressel, Jacob</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5867-0224">0000-0001-5867-0224</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ottenbreit-Leftwich%2C+Anne%22">Ottenbreit-Leftwich, Anne</searchLink><br /><searchLink fieldCode="AR" term="%22Jantaraweragul%2C+Katie%22">Jantaraweragul, Katie</searchLink><br /><searchLink fieldCode="AR" term="%22Jeon%2C+Minji%22">Jeon, Minji</searchLink><br /><searchLink fieldCode="AR" term="%22Warner%2C+Jayce%22">Warner, Jayce</searchLink><br /><searchLink fieldCode="AR" term="%22Brown%2C+Matthew%22">Brown, Matthew</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22TechTrends%3A+Linking+Research+and+Practice+to+Improve+Learning%22"><i>TechTrends: Linking Research and Practice to Improve Learning</i></searchLink>. May 2022 66(3):412-422.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 11
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2022
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Teacher+Certification%22">Teacher Certification</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+Education%22">Computer Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+Location%22">Geographic Location</searchLink><br /><searchLink fieldCode="DE" term="%22Institutional+Characteristics%22">Institutional Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Differences%22">Racial Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+Status%22">Socioeconomic Status</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Competencies%22">Teacher Competencies</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Equal+Education%22">Equal Education</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Indiana%22">Indiana</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11528-022-00726-9
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 8756-3894
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study aims to investigate the licensure status of computer science teachers in Indiana by examining teacher licensure data as it relates to school locale and school demographics across the state. Results indicate that there is no significant difference in the presence of teachers with CS-Related or Approved licenses across schools with various locale designations and demographic (ethnic and economic) compositions. It is a positive indication that there are no glaring disparities in access to well-prepared CS teachers across student groups. This also provides an opportunity to investigate additional factors that may shed light on future areas of focus.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2022
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1336973
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1336973
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11528-022-00726-9
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 412
    Subjects:
      – SubjectFull: Teacher Certification
        Type: general
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Geographic Location
        Type: general
      – SubjectFull: Institutional Characteristics
        Type: general
      – SubjectFull: Ethnicity
        Type: general
      – SubjectFull: Racial Differences
        Type: general
      – SubjectFull: Socioeconomic Status
        Type: general
      – SubjectFull: Teacher Competencies
        Type: general
      – SubjectFull: Student Characteristics
        Type: general
      – SubjectFull: Equal Education
        Type: general
      – SubjectFull: Indiana
        Type: general
    Titles:
      – TitleFull: Investigating CS Teacher Licensure in Indiana
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Koressel, Jacob
      – PersonEntity:
          Name:
            NameFull: Ottenbreit-Leftwich, Anne
      – PersonEntity:
          Name:
            NameFull: Jantaraweragul, Katie
      – PersonEntity:
          Name:
            NameFull: Jeon, Minji
      – PersonEntity:
          Name:
            NameFull: Warner, Jayce
      – PersonEntity:
          Name:
            NameFull: Brown, Matthew
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 8756-3894
          Numbering:
            – Type: volume
              Value: 66
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: TechTrends: Linking Research and Practice to Improve Learning
              Type: main
ResultId 1