MAPPING HELP-SEEKING 1 Mapping the Inequity


1. Mapping the Inequity Implications of Help-Seeking in Online Credit-Recovery Classrooms. Jennifer ... frequently, instructors identified students' current level of understanding, worked through ... student-teacher ratios and in labs with one or more certified teacher. ..... assessment question into Google to find the answers.

MAPPING HELP-SEEKING

Mapping the Inequity Implications of Help-Seeking in Online Credit-Recovery Classrooms

Jennifer Darling-Aduana,* Annalee Good, & Carolyn J. Heinrich

* Corresponding author: Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN; Phone: 847 372 5782; Email: [email protected]

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2 Abstract

Background/Context: Expectations for student help-seeking contribute to inequitable access to quality learning experiences in traditional classroom settings. Parallels and implications of helpseeking in online courses have yet to be examined systemically. Purpose: This study extends current literature by mapping the nature of help-seeking interactions between students and teachers in online credit-recovery classrooms. We highlight patterns in these interactions and spaces associated with disparities in academic opportunities in traditional classroom settings. Setting: Data were collected in a large, urban school district in the Midwest that serves a student population predominantly of color and from low-income backgrounds. Research Design: We draw on both qualitative and quantitative data, including 156 observations and 24 interviews collected across three school years. The observation instrument contains indicators that capture the type of interactions occurring, as well as narrative comments and vignettes. Conclusions/Recommendations: While our findings suggested the assignment of students to online credit-recovery courses may reproduce patterns in inequitable access to quality educational opportunities, we identified strategies to support more equitable learning through online courses. Teachers should provide explicit expectations and proactive assistance to students. The use of real-time data, low student-teacher ratios, and assigning teachers certified in course subjects would likely also improve educational quality.

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3 Executive Summary

Seventy-five percent of U.S. school districts offer online classes and credit recovery is one of the most common uses of online platforms. The explosion of online credit recovery programs prompts important questions about the relative quality of instruction, as well as critical implications for equity. In this paper, we examine the changing role of student-teacher interactions, specifically help-seeking, in these online classrooms and propose several suggestions for improving equitable student access to quality educational opportunities by amending current interactional norms and expectations. This is the first study we know of to apply social reproduction scholarship to online classrooms and is intended to provide a motivation and empirical guide for future research on this topic. Specifically, we examined the following research questions: (1) What are the potential spaces for student-teacher interactions in a digital credit-recovery program, and (2) What are specific patterns in help-seeking interactions between students and teachers in the same online credit recovery program? Research Design Our findings drew on data from a longitudinal, mixed methods study on the implementation and outcomes associated with the use of digital educational tools. Data were collected in a large, Midwestern school district. All observation data were collected using a standardized, research-based observation protocol. The qualitative analysis relied primarily on narrative vignettes of classroom interactions collected through 156 observations across 18 schools, which we supplemented with 24 interviews with credit-recovery instructors and the quantitative analysis of rubric-style ratings of classroom facets such as instruction, interactions, and engagement. We analyzed the qualitative data first using thematic nodes followed by inductive coding. Subsequent excerpts were then used to confirm, revise, or add detail to

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previous codes. Triangulation across qualitative and quantitative data was used to corroborate the validity and reliability of the resulting analytic themes. Due to the ordinal nature of the observation scales employed, we used chi-squared tests and ANOVAs to identify significant differences between groups in our supplemental, quantitative analysis of classroom ratings. Findings In credit-recovery labs, student learning occurred primarily through interactions with the online course platform. However, most modifications or individualization of the standardized course structure required facilitation by a live instructor, with instructor assistance mediating access to quality educational opportunities. Most students gained instructor assistance by asking for help, a self-regulation strategy associated with middle-class parenting and cultural norms, raising concerns that students may receive differential access to individualization facilitated by instructors based on ascriptive characteristics. Students who stayed consistently on-task during class periods and completed courses often differed from the general credit-recovery student population. Successful students were more likely to have access to digital devices and Internet outside of school and demonstrate requisite skills, such as minimum reading proficiency and study skills. However, most students struggled in silence or sought the assistance of online resources, which often provided content support sufficient to answer an assessment question but did not scaffold material to enable mastery. Nonetheless, online help-seeking appeared to prevent student demoralization and supported course progression, if not learning. Instructors also typically focused on assessment assistance when asked for help. Less frequently, instructors identified students' current level of understanding, worked through problems with students, or retaught content. Both assessment and deeper, learning-focused

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assistance were provided most often to students who requested help, although we observed some students fail to gain teacher attention when demonstrating less assertiveness in their requests. Instructors were also more likely to offer assistance to students who previously requested help during the class period. Interviews with teachers indicated this reactivity might be a result of their belief that students understand classroom expectations and the behaviors required to be successful. We observed more, and more learning-focused, interactions in classrooms with lower student-teacher ratios and in labs with one or more certified teacher. Although observed less frequently, we identified three promising strategies for minimizing barriers to learning in online classrooms: (1) building trust, (2) offering assistance, and (3) providing content-specific expertise. We observed more help-seeking in labs where teachers demonstrated respect and interest in their students' lives, as well as more favorable interactions in classrooms where teachers used digital tools to monitor student engagement and learning. Often the use of technology in this manner was accompanied by offers of assistance to students not making adequate course progress. In other classrooms, teachers proactively reached out to each student to discuss learning, course progress, and goals in conjunction with or separate from progress monitoring reports provided through the course system. Lastly, some schools assigned students taking courses in a single subject to a classroom with a teacher certified in that area. We observed more frequent help-seeking and a greater focus on instructional versus testassistance in these classrooms. Conclusions This study extends current literature on spaces of educational inequality by mapping help-seeking interactions between students and teachers in online credit-recovery labs to spaces associated with disparities in academic opportunities and attainment in traditional classroom

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settings. Based on our findings, we offer suggestions to improve equitable student access to quality learning opportunities in online-credit recovery classrooms. ● Instructors should provide explicit expectations and proactive assistance to students, with students most likely ask for and accept help if instructors demonstrate trustworthiness and respect. ● The use of technological tools and real-time data can facilitate student-teacher interactions, such as goal setting and targeted support. ● Low student-teacher ratios and assigning teachers certified in course subjects appears to improve educational quality. Our findings and recommendations have application for the practice and policy of online creditrecovery courses. We also aim with this study to provide an empirical guide for mapping social reproduction research (developed based on traditional classroom structures) to the rapidly expanding online instructional context.

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7 Introduction

We know the interactions students and teachers have within their classrooms help define learning opportunities, and these interactions are shaped in part by the social identities and cultural contexts of both students and teachers (Downey & Pribesh, 2004; Lareau & Weininger, 2003; Rist, 1970). We also know online learning fundamentally changes the instructional space students experience in school, and therefore, the nature of these critical interactions. With over 75 percent of U.S. school districts serving one or more students enrolled in an online course (Gemin, Pape, Vashaw, & Watson, 2015), it is critical to consider how the growth of online courses changes how students interact in classrooms and how these interactions might reflect, exacerbate, and/or mitigate persistent racial and socioeconomic inequalities in schools. The purpose of this study is to examine interactions between students and teachers in online credit recovery labs within a large, urban, Midwestern school district, with an emphasis on "helpseeking" (student attempts to gain assistance from teachers). Existing research on digital learning prompts questions about the nature of online classroom interactions. For example, despite trends of technology-based instruction replacing several of the central tasks traditionally assigned to teachers, some research suggests that online learning programs that incorporate live instructors achieve better student outcomes (Hannum, Irvin, Lei, & Farmer, 2008; Means, Toyama, Murphy, & Baki, 2013; Taylor, Clements, Heppen, Rickles, Sorensen, Walters, & Allensworth, 2016; Zhao, Lei, Yan, Tan, & Lai, 2005). This research indicates an important, but not yet fully understood role, for interactions in mediating student access to learning in online spaces. The stakes for students enrolled in online credit recovery courses are particularly high. With growth in online recovery programs rapidly expanding and high school completion a prerequisite for most employment (Torpey & Watson,

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2014), broadening understanding of the role of interactions in mediating student access to quality educational experiences are needed to inform practice and policies in these settings. Credit recovery is one of the most common uses of online platforms (Clements, Stafford, Pazzaglia, & Jacobs 2015; Queen & Lewis 2011), providing students opportunity to earn previously incomplete course credits required for high school graduation. In most instances, vendors develop the curriculum for their software (Patrick, Kennedy, & Powell, 2013), raising potential concerns about adaptability and relevance, particularly for students who may have not yet mastered grade-level content, fully developed self-regulated learning strategies, or struggled previously with engagement. With students identified as racial/ethnic minorities and from low socioeconomic status backgrounds more likely to be at risk of dropping out than students in the general population (Rumberger, 2004), the extent to which online credit recovery programs provide access to quality educational experiences for students has relevance for understanding the creation and maintenance of gaps in academic achievement and attainment. Other concerns have been raised that with little oversight, education standards in online credit recovery programs may be lowered, and at-risk students may be directed into online learning as a means of costsavings, potentially further exacerbating unequal access to quality learning opportunities (Gardiner, 2014; Thevenot & Butrymowicz, 2010). Drawing on interpretive social reproduction scholarship (Mehan, 1992; Lareau & Weininger, 2003; Rist, 1970), we examine the following research questions: 1. What are the potential spaces for student-teacher interactions in a digital creditrecovery program? 2. What are specific patterns in help-seeking interactions between students and teachers in the same online credit recovery program?

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We then explore possible implications, providing a guide for future empirical research on inequity in online credit-recovery courses by building upon research on interactions in traditional classrooms. This study extends current literature by mapping interactions between students and teachers in online learning to spaces associated with disparities in academic opportunities and attainment in traditional classroom settings (Calarco, 2011; Downey & Pribesh, 2004; Heath, 1982; Ladson-Billings, 2004; McLaren, 1994; Rist, 1970).1 As an increasing number of students receive a portion or most instruction online, we must expand our scholarship to encompass these new learning environments, particularly when these shifts disproportionately affect students already underserved by current educational systems. Help-Seeking as a Conceptual Framework Prior research indicates that student-teacher interactions matter, and they matter differently based on social identities (Calarco, 2011; Downey & Pribesh, 2004; Heath, 1982; Ladson-Billings, 2004; McLaren, 1994; Rist, 1970). The following literature identifies the extent to which a form of teacher-student interactions - help-seeking - may be influenced by cultural signals. Addressing concerns raised by others (i.e., Peck, Hewitt, Mullen, Lashley, Eldridge, & Douglas, 2015) that the structure and limited personalization and human interaction in online credit-recovery courses may only serve to reproduce current class categorizations, our study draws on interpretive social reproduction scholarship (Mehan, 1992; Lareau & Weininger, 2003; Rist, 1970) to guide an examination of the nature of student-teacher help-seeking interactions in online spaces. This study furthers understanding into how these online course-based interactions may advantage (or disadvantage) students from various backgrounds. The increased prevalence of online courses in the U.S. has the potential to disrupt current, systematically biased interactional norms and expectations by modifying how students and

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teachers interact in an instructional setting. This disruption may take several forms with different implications for inequity. Online courses fundamentally reframe the role of the teacher, as teachers are no longer primarily responsible for content delivery. Larger class sizes and fewer, more constrained student-teacher interactions may lead teachers to rely more often on unconscious cultural cues when interpreting student behaviors and actions (Altonji & Pierret, 2001; Robinson & Lubienski, 2011). Accordingly, student help-seeking, a self-regulatory skill often taught through middle class parenting strategies (Calarco, 2011; Lareau, 2003; Streib, 2011), may play an increased role in shaping how teachers interact with, assist, and evaluate students in an online classroom setting (Ahn, 2011). Alternatively, there is reason to believe elements of online courses may equalize the quantity and quality of student-teacher interactions. The availability of real-time data and frequent assessments typical of online courses may reduce reliance on incomplete or inaccurate information based on cultural signals (Altonji & Pierret, 2001; Devine, Forscher, Austin, & Cox, 2012). The use of standardized, asynchronously delivered course content may allow instructors to focus on encouraging the development of learning and study skills, such as note-taking. Decreased time devoted to direct instruction and standardized course structures may also facilitate the more explicit communication of expectations, eliminating cultural “insider knowledge” on how best to learn and earn course credit (Bernstein, 1975; Delpit, 2006). What help-seeking looks like may also vary in online versus traditional classroom settings, as students enrolled in online courses have ready access to additional non-instructor based resources for assistance, such as the educational program delivering content and Internet resources. By presenting a descriptive analysis of patterns in student help-seeking and subsequent studentteacher interactions, we explore which and in what contexts these hypothesized disruptions to

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classroom interactional norms and expectations appeared across credit recovery labs in a large, urban district. Social reproduction theory frames schools as a societal institution, where teacher expectations reflect cultural norms, advantaging students from dominant groups (Bourdieu, 1986/2010; Lareau, 2003; Van den Bergh, Denessen, Hornstra, Voeten, & Holland, 2010). The result is social reproduction disguised and legitimized through the appearance of school success as a factor of individual, versus class-specific, characteristics (Bourdieu, 1986/2010; Lareau & Calarco, 2012). As social actors in social institutions that tend to identify with middle-class norms, teachers spend more time with and react more positively to the interactional styles and language patterns exhibited by middle-class students (Bernstein, 1975; Calarco, 2011; Streib, 2011). For instance, many teachers equate student engagement and intelligence with active participation in classroom activities, verbal assertiveness, and help-seeking behaviors, all which can be differentially expressed based on socioeconomic status, racial identity, and cultural background (Calarco, 2011; Heath, 1982; Lareau, 2003). Although associated with student background, students' "cultural toolkit" of attitudes, behaviors, and preferences are perceived by others, including teachers, as individual skills, talents, or capacities (Bourdieu, 1986/2010). For instance, teachers may prioritize students who ask more questions because teachers view them as more interested in learning, even though research demonstrates that help-seeking may reflect class-background more than academic engagement (Calarco, 2011, 2014). Help-seeking is a rehearsal learning strategy, whereby students must correctly evaluate their need for help and actively communicate with an individual capable of assisting (Newman, 2000; Zimmerman, 2008). Effective help-seeking thus requires many interpersonal attributes: communication skills, assertiveness, and the ability to identify when and from whom to request

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assistance (Calarco, 2011, 2014). As a result, help-seeking often supports students in staying ontask, increases the speed of learning, and builds confidence (Calarco, 2011; Newman 2000). Beyond cultural norms, the decision to ask for help is also informed by perceptions of trust, relational style, and expertise, with students more likely to ask for assistance if they believe an instructor will treat them with respect and communicate pertinent information effectively (BrionMeisels, 2015, 2016). While teachers expect students to be proactive and seek out help when struggling, this expectation is predominantly expressed implicitly (Ahn, 2011; Calarco, 2011; Patrick, Lynley, Ryan, Edelin, & Midgley, 2001). Delpit (2006) established that when these implicit rules are made explicit, all students are better able to succeed (i.e., Parks, 2010; Luykx, Lee, Mahotiere, Lester, Hart, & Deaktor, 2007). In this regard, the standardized structure and rules of labs supporting online instruction offer many advantages by making the implicit expectations in traditional classrooms more explicit. Instead of focusing predominantly on teaching, teachers can, and do, prioritize maintaining student motivation in online classrooms (Ahn, 2011). More explicit expectations about weekly course progress and what type of learning is required may also be communicated more efficiently due to standardized course structures and requirements. Even if an instructor does not communicate these expectations, students do not have to adjust to changing teacher expectations or the subtle situational differences that affect interpersonal interactions as often as in traditional classrooms (Arnot & Reay, 2007; Calarco, 2014). A final, important implication of interactions within computer-based instruction are changes in the quantity and quality of information about students accessible to educators. Often, teachers have less access to information through in-person interactions in online classroom settings but increased access to information on student progress and assessment results. When

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lacking information, evaluators often unconsciously employ statistical discrimination, relying on average characteristics of others belonging to a similar socio-demographic group (Altonji & Pierret, 2001; Ewens, Tomlin, & Wang, 2014). Statistical discrimination reinforces the existing status-quo and disadvantages historically lower-achieving subgroups. However, the effect fades as evaluators gain access to information about an individual, at which time evaluators substitute knowledge about the individual for average group characteristics (Altonji & Pierret, 2001; Devine et al., 2012). Specific to this study, we are interested in whether access to presumably more objective information on students mediates the extent to which teachers rely on social and cultural cues when initiating or responding to help-seeking requests. In this paper, we apply frameworks from prior research on the role of help-seeking in traditional school settings to the new digital, online context, which has yet to be fully explored, and indeed, may be more challenging to observe. We examine how changing student-teacher interactional norms, expectations for both students and teachers, and access to information may redefine previous spaces of inequity and highlight possible levers for improving student access to quality education with digital tools. Research Design Situated within a multi-year, mixed methods study on the implementation and outcomes associated with digital tools in K-12 classrooms in the United States, this paper draws on 156 qualitative observations of instructional sessions and 24 interviews. Data were collected across three years and 18 schools implementing an online credit recovery program in a large, urban school district in the Midwest. We collected 17 observations across two high schools during the 2014-15 school year, 31 observations across seven high schools during the 2015-16 school year, and 108 observations across 18 high schools during the 2016-17 school year. Within the district,

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approximately 78 percent of high school students received free or reduced lunch, and 61 percent of students were identified as African American, 21 percent as Hispanic, 11 percent as White, and seven percent as Asian, multiracial, or another race or ethnicity. Around one-fourth of all high school students in the district accessed one or more courses online, with the students enrolled in online courses slightly more likely to be identified as African American and from low-income backgrounds. The well-tested, research-based observation instrument (Author, 2016) enabled observers to evaluate the extent to which an instructional session (and integration of educational technology) facilitated quality learning opportunities for students. The observation instrument contains a set of indicators or dimensions of quality elements that capture the type of interactions occurring between teachers, students, and educational technology (when in use). We recorded ratings of ten core elements of digital and blended instruction (described in Appendix A) on a 04 (5-point) scale. Observers also recorded narrative comments and vignettes, as well as information on the total instructional time, time on task, time a student interacted with an instructor, and whether the format facilitated live interaction between instructors and students around instructional tasks. Although we documented descriptors of students and teachers within the observation instrument, including estimations of gender, race, and ethnicity, we do not report these categories in our analysis of qualitative data, as these were based on researcher judgments versus self-identification. We also facilitated regular training to establish interrater consistency for all raters conducting classroom observations. We analyzed qualitative data from these 156 observations and 24 interviews in NVivo coding software using thematic nodes including physical environment, curriculum, instructional model, interactions, assessment, engagement, digital citizenship, and digital tools. Spot-checking

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was used to check coding consistency. We followed with inductive coding, reading and assigning labels and thoughts to each excerpt. After saturation was achieved, subsequent passages were used to confirm and add detail to previous codes; we also searched for exceptions and alternative explanations to challenge preconceptions and personal biases. Triangulation across qualitative and quantitative data was used to confirm the validity and reliability of the resulting analytic themes. Quantitative analyses were conducted to test and supplement emergent findings. Due to the ordinal nature of the observation scales employed, we used chi-squared tests and ANOVAs to identify significant differences between groups. Summary of Findings In this section, we illustrate potential spaces for student-teacher interactions focused on help-seeking within online credit-recovery classrooms. Drawing on rich, observational data, we highlight patterns, focusing on interactional strategies that may either reproduce or mitigate gaps in student access to quality educational opportunities. Prototypical Help-Seeking In all credit recovery labs observed, students were provided a laptop or desktop computer and expected to progress through the online program independently. Course progression required watching video lectures, responding online via clicks and written responses, and taking notes. The labs were supervised by one or more teachers whose primary role was to ensure students were making progress in the course. These instructors also provided technical support and on occasion instructional support. Learning occurred primarily through student interactions with the online course platform, which housed and delivered the curricular content. Once students logged in, content relevance, cognitive demands, and feedback informed subsequent student engagement, self-regulation, and persistence. Students largely determined their pacing and could

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repeat sections with instructor permission. The software also offered lecture notes in multiple languages. Teachers described in-program accommodations as minimal; most involved teacherinitiated actions such as removing multiple-choice options in quizzes or resetting lessons so that students could attempt them again. Any further adaptation to students' needs, interests, or context had to be facilitated by a live instructor, with any differential access to instructors resulting in disparate access to equitable educational opportunities. Below is a composite vignette that contains observation notes from several classrooms and is representative of our qualitative data. The vignette was created to illustrate the instructional setting and interactions of a typical computer lab reserved for students enrolled in online courses. The vignette also serves as a foil in discussions of variants of the instructional models and settings observed. At the beginning of the first period, students straggle in and go directly to the desktops. There are 30 computers in the large basement classroom. All students sit at their desktop computers, working on various course modules that depend on where they need to recover credit. Twelve of the 15 students have headphones on and plugged into the computer. Students are talking quietly, occasionally laughing. Ten minutes into the class period, the teacher stands up and walks around to check on the students, at which point nine of 15 students are actively working in the online course system. The teacher emphasizes to the students that they need to strive for the goal of completing three percent of their coursework per week. He tells them to focus more and to take advantage of the resources they have both during and after the school day. The students are a distraction to each other, with some students walking around and disturbing others or talking out loud. There is no redirection of students on the part of the teacher. Toward the

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end of the period, five of the students are still actively clicking, looking up at the screen, typing, etc. Two of these students also have a paper notebook out. These students are engaged in an iterative process of reading content off the screen and then writing it down in their notebooks. One student is toggling between the online course program and Google to look up terms. At any one time, four to five students are checking their phones, and one or more are sleeping. Typical of many observations, the instructor in the above vignette interacted with students in a predominately motivational versus instructional role. The extent to which instructors monitored student engagement varied across classrooms. Above, the instructor did not attempt to redirect students, while in other observations instructors verbally redirected students, often with limited success. When students did not seek help from teachers. As shown in the vignette above and across observations, student time off-task increased, and interactions with the online course system decreased substantially over the observation period. Few students maintained the focus to take consistent advantage of the educational resources available. Examples of fully engaged students were somewhat rare but were observed. One such student from an observation in an alternative school setting,2 "worked through the assessment questions, checking her notes and selecting responses carefully," without interruption and without interacting with any instructors or peers in the classroom environment. The student possessed sufficient self-regulation skills, including focus and persistence, that she maintained productive interactions with the software interface. Furthermore, she appeared to possess requisite academic skills, such as minimum reading proficiency and study skills, further facilitating access to course content.

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The student described above was atypical in successfully accessing and interacting with course content without requiring instructor assistance. Within this select group, many students completed coursework outside of the school day, indicating home access to digital devices and the Internet, as well as minimal need for instructor assistance to master content. Our quantitative analysis of student behaviors within the online system suggested that those accessing the program at home, outside of school hours were less likely to qualify for free or reduced lunch, more likely to have achieved junior or senior standing, and more likely to have scored highly on previous standardized assessments (Author, 2017). When examined in conjunction with barriers to learning, the typical profile of students who ultimately earned credit highlights the many ways in which transitioning from teacher-driven to technology-driven courses may further disadvantage those students most in need of additional assistance, amplifying current disparities in achievement. In the discussion and illustration of more typical interactional patterns below, we focus predominantly on the experiences of those students requiring assistance to learn course content. We detailed the observed interactional pathways of students struggling to learn content in Figure 1, highlighting how interactions with lab instructors may have mediated learning for these students. Whether a student decided to ask for help or completed coursework without assistance, there were barriers to learning and opportunities for demoralization and subsequent disengagement. When students didn't ask for help, they often required more time to finish assignments or were unable to learn content. During one observation in an alternative school, a student and teacher "worked together and found that the program's supposed-to-be correct answer is not correct," after a student voluntarily asked for help. A student who didn't ask for assistance on the same problem would likely have either learned the content incorrectly or been

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unable to complete the assignment. In the following example, a student attending a different specialty school did not request assistance despite appearing unable to complete the required task. At the time the observation began, the student was working in a Thermochemical Equations course. During the 20 minutes observed (before the class change), he progressed slowly in the lesson. In particular, he seemed to stall in the activity where more self-initiative was required (to practice the enthalpy of reaction equations). He did not leave the computer but did not practice what he had been shown in the video (solving problems). He did not request any assistance. Whether the students’ slow progress was due to low engagement or difficulty comprehending content, a proactive instructor could have diagnosed and mitigated the underlying issue. Instead, the student received no credit for his time in the platform and left at the end of the class period without appearing to master content. In addition to often taking longer, the learning trajectories of students without instructor assistance was filled with opportunities for demoralization. Most of these students had some constructive interactions with the software but progressed through course content slowly, with frequent distractions. For instance, in the representative, composite vignette presented at the beginning of this section, nine of 15 students interacted with the online course system at the beginning of the class period, with approximately half disengaging as the class period progressed. One of the primary means by which students struggling with course content made progress without instructor assistance was through Internet searches or guessing. One such student from an observation in an alternative school setting read a source document and took notes on a lesson on the Mongol Empire before beginning an assessment about halfway through

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the observation. When completing the assessment, "the student copied and pasted the exact assessment question into Google to find the answers." We observed similar behavior frequently across settings, which suggests a different type of help-seeking than the traditional version between students and teachers. Easier access to online resource used in this manner might result in assessment scores that don't reflect learning. In the instance above, the student's course notes may have been insufficient or required more effort to review than an Internet search; in other similar cases, students choose not to take notes at all, despite district policy guidance that urged classroom instructors to enforce note-taking practices. Notwithstanding concerns raised by this strategy, students who completed online assessments in this manner made course progress and might avoid demoralization. If the goal of credit-recovery is solely to provide students a second chance to earn course credits required for graduation, then this process achieves that goal. If the goal of credit recovery is to give students a second opportunity to learn course content because mastery of that material is deemed necessary for post-secondary success, then help-seeking only from online sources often did not contribute toward that end. When students did seek (and receive) help from teachers. Similar pathways emerged among students who asked a teacher for help. Within a given classroom, student help-seeking and subsequent differential treatment by instructors often influenced the frequency and quality of student-teacher interactions. Many observations that identified students asking for assistance were accompanied by comments indicating reactive instructor behavior, such as sitting behind a computer at the front of the classroom. One instructor shared his instructional strategy as follows, “I tend to stay in the back, watch what they are doing, help as needed.” In other observations, instructors focused on classroom management and administrative tasks unless a “student voluntarily asked the instructors to check their answers or help with the questions." As

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such, the format of the online course system often required more initiative on the part of participating students than traditional instruction methods. A passive participant in a class incorporating a lecture component might still absorb knowledge, while the same student in a classroom that allowed students to determine their pacing is more sensitive to low student engagement or motivation (Ahn, 2011). As demonstrated in the passages above, most instructors focused their attention on students who actively voiced the need for assistance, frequently resulting in inequitable access to one of the students' most valuable instructional resources – instructor attention. As expected, we found a strong association between interaction and instruction ratings in our quantitative analysis of the observation data. On both dimensions, about 77 percent of the observations were rated a "2" on a zero to four-point scale, and a chi-square test confirmed the statistically significant association (p = 0.000). An interaction rating of two indicated that instructors or resources had some constructive interaction with students, compared to mostly (3) or constant constructive interactions (4), no constructive interactions (1), or destructive interactions (0). For instruction, a rating of two indicated that the instructional model and tasks facilitated some quality learning opportunities but do not adapt to observed (or known) student needs. The distribution of ratings of interactions in individual student observations differed from that of whole-class observations (p=0.006). There were noticeably more (21 vs. 5 percent) low ratings in observations of individual student's learning experiences. This disparity was supported by observation notes, many of which explained that instructors interacted with students in a manner that enhanced learning throughout the class period. However, few instructors supported the instruction of most students in their classroom during any given observation.

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Instructors responded to most but not all observed requests for assistance by students. In one classroom, "The student asks the instructor for assistance about 44 minutes into the observation, but the teacher doesn't hear her. At the end of the observation, she is waiting by the teacher's desk for assistance." In another example, we observed an instructor repeatedly respond to requests for assistance from the one student identified as gifted in her classroom, limiting the teacher's ability to assist the students in her lab working on credit recovery. These findings are consistent with Calarco's (2011) finding that teachers provided more assistance to students with cultural capital associated with dominant groups, who were more likely to request help repeatedly and make eye contact and speak loudly while doing so. Even in instances where teachers proactively sought out students, teachers were more likely to follow up with students who had previously asked for help. We observed this method of student identification most often when students asked for assistance early in the class period, as seen in the following excerpt. The student was stationary for a few minutes and then went up to the front to ask the teacher a question. Another support teacher came around and noted that the student was at a 40 percent quiz score. He took some time to discuss the content with the student and to help him in considering the answers to a particular question. He encouraged the student to apply his test-taking skills, e.g., to determine the solution through a process of elimination. At the conclusion of the observation, the student was still working on the quiz, and he went up to the front to ask the teacher a question. Asking for assistance earlier in the class period, as the student did above, might prime teachers to see the student as needing assistance or signal student engagement, indicating that time invested in assisting the student would likely translate into achievement. Similarly, although we do not

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have sufficient information to indicate directionality, there was a strong, statistically significant positive relationship between interaction and student engagement ratings (p = 0.000). These patterns appear to advantage further those students who asked for and gained instructor assistance. Another reason many teachers relied on students to ask for help may have been that most teachers believed program expectations were clear to students. One teacher shared in an interview, "Teachers don't typically have a plan. Teachers refer to their online course system screen as students come in. Students know what they need to work on and are supposed to get started on it." In an observation where a new student was assigned to the online course system, the teacher set the student up with a login. The entire orientation process involved only a few minutes of student-teacher interaction and was focused on the technical components of the platform. Without explicit guidelines in all online labs, the program model required student intuition to determine how to use the available resources most effectively. Keeping expectations surrounding course completion and help-seeking implicit appeared to disadvantaged students who did not know those expectations and with the least prior experience or success in dominant cultural settings (Bernstein, 1975; Delpit, 2006; Mehan, 1992). Access to instructional assistance may also have varied based on the number of students and instructors assigned to each online credit-recovery lab, as more instructors mean more students could hypothetically receive individualized assistance at a given time. We observed a variety of student-teacher ratios across the 18 schools, with ratios ranging from 2:1 to 28:1. On average, we observed a student-teacher ratio of 10:1 in specialized and alternative schools compared to 14:1 in neighborhood schools. In whole classroom observations, we saw a definitive pattern and statistically significant association between larger student-teacher ratios and lower

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ratings of digital citizenship, defined as responsible use of the technology by students, among students in the classroom, likely due at least in part to classroom management issues. Ratings of the classroom environment, which included considerations of who else in the physical environment was available to assist students with technological problems and support learning, were also significantly (positively) associated with digital citizenship (p=0.013). These descriptive results suggest that schools may wish to prioritize a lower student-teacher ratio in online instructional environments to increase the number of students who might receive assistance and accommodations from instructors at a given time. Type of help received. The most common reason that students asked for help was to ask a teacher to check their quizzes. As district policy only allowed students two quiz retakes, many students asked teachers to check quiz responses before submitting assessments for online grading, a process that was systematized by the district in the 2016-17 school year. The new policy required teachers to review quiz answers with students before submitting responses to encourage instructional assistance and improve student pass rates. The most frequently observed response to this policy was to encourage students to engage in the process of elimination when completing quizzes, with many students asking an instructor to review responses two or more times during a single period, despite that fact that most quizzes consisted of multiple choice questions with four answer options. The following excerpt describes one of many students who used this process strategically to progress. The student spent some of the class period with videos running and answering problems, but she was quickly distracted. She talked with classmates, used her phone, and did not have headphones in to hear the audio. She made minimal progress in the videos. After filling in answers to the assessment (mostly incorrect), she went up to the teacher's desk

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multiple times for a list of the questions that she had incorrectly answered before changing them and going back to check again. She did not spend a lot of time thinking about the problems she previously answered incorrectly. Many observations highlighted a systematized process similar to the one described above where teachers wrote or verbally shared the numbers of the questions students answered incorrectly without providing accompanying instructional support. In some instances, we observed classroom instructors staying at their desks and calling out question numbers, and on more rare occasions, providing answers. Often, lines formed near the end of the class period as students worked to complete an assessment, with the same students standing in line multiple times until they determined the correct answers to the predominantly multiple-choice assessment questions through process of elimination. This type of interaction did not support student learning, with no assistance provided on how to find or learn content, only how to correctly respond to assessment questions. Alternatively, although less frequent, some instructors offered instructional assistance in response to requests to review quizzes. As previously mentioned about online test assistance, access to in-person test assistance often prevented demoralization and facilitated course progression. For this reason, test assistance without instructional assistance may be preferable to no student-teacher interactions. At the same time, there is an opportunity when students initiate contact in this manner to use assessment results to inform teaching moments that encourage not just course progression, but also content mastery. Among students who obtained assistance, interactions with teachers might be either test or learning focused, demonstrated by the pathway fork in Figure 1. As highlighted above, test assistance included providing students information on which questions they answered incorrectly

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but may also include providing students the answers to assessment questions. Learning assistance included scaffolding knowledge or problem-solving with students to access and digest content. Whether students received learning assistance depended on their instructors' content and instructional capacity. Student access to qualified and experienced instructors varied across classrooms. Observations indicated that instructors were often unable to assist students with content-related questions. One instructor shared in an interview that to help a student with a genetics module, the student and teachers used YouTube and Internet searches to find the answer. In the excerpt below, we observed two teachers attempt to assist a student. Without sufficient content knowledge, the teachers spent most of the class period searching for the answer. The student raised her hand and requested assistance from a teacher at the beginning of the observation time. The first teacher is unable to assist. The teacher copies and pastes the question in Google and attempts to find resources. The teacher then asks the other teacher for assistance. The second teacher takes some time to review the project and is able to find the answer to one of the problems. When the teachers left, the student reverted to playing with her phone or watching a TV show. The teachers left and returned numerous times, only finding the answer to one question throughout the class period. Instead of involving the student in the learning process, the teachers tracked down the answer alone while the student waited, playing with her phone. We found that even when teachers were familiar with content, they rarely helped students learn the material, providing answers instead. In the above excerpt, the teachers lacked not only the content knowledge but also the expertise or belief that they should instruct students on the process of learning. After repeated experiences like the one described above, it is possible that many students might decide there is little value in

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requesting instructor assistance, decreasing subsequent help-seeking (Brion-Meisels, 2015, 2016). Further supporting this assertion, we observed more favorable rates of instructor engagement, digital citizenship, interactions, student engagement, and physical environment in classrooms where we noted in our observations that students had access to one or more certified teachers. The sizeable proportion of substitute teachers serving as instructors in credit recovery labs may contribute to the variability in prior experience and qualifications observed. We identified substitute teachers in 18 percent of observations where we had information on instructor background (n=77). The presence of a substitute teacher, whether long-term or singleday, in a credit recovery classroom, was strongly, significantly associated with less favorable ratings of instructor engagement; 86 percent of observations with a substitute teacher received the lowest ratings (0 or 1) on instructor engagement, compared to 38 percent of observations without a substitute (p=0.004). Ratings of instructor-student-digital tool interactions were also significantly lower in classrooms with a substitute teacher (p=0.002). In one class, an observer noted, the "students weren't accessing the software during the session, primarily because it was a sub that day who didn't have access to the program and couldn't help." In another observation, a substitute teacher refrained from monitoring student engagement to prevent "starting something," communicating low expectations in the process. In a more extreme case, "The substitute teacher did not play an active role and at some point, just left the classroom." These instances highlight the limited capabilities of some substitute teachers, with the sizeable proportion of classrooms served by substitute teachers suggesting possible discrepancies in access to quality learning experiences between credit recovery and general education classrooms. Atypical, but Promising, Strategies

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Above, we discussed how variations in student-teacher interactions might have created unequal access to quality learning opportunities. Below, we highlight interactions that minimized or eliminated many of the previously discussed barriers to learning, which if applied more universally may reduce educational inequities in these and similar spaces. Although exceptional rather than the norm, the following classroom observation highlights all three characteristics that instructors used to facilitate more equitable access to quality learning opportunities in online credit recovery labs: (1) systematically building trust, (2) consistently offering assistance, and (3) providing content-specific expertise. The instructor rotates around the room a number of times, asking each student if everything is going okay. There is quiet talking. A pair of students working together calls the instructor over, who works through problems with them using process of elimination and explaining underlying concepts (in fluent Spanish). When the instructor finishes working with the students, he rotates the room, checking in with the other students in the room again before returning to the English Learner students. About ten minutes into the lesson, the instructor sits at his desk for the first time. A few minutes later a different student calls the teacher over to help his friend who failed a test. The instructor reviews responses with the student, focusing on the underlying content, which the teacher says the student gets before encouraging him to apply that knowledge to the quiz questions. The instructor then rotates around the room checking in with students who haven't yet asked for help. During the observation, the instructor had two extended conversations with students, one where a student explained a connection he made between his geometry assignment and a personal interest and the other where the instructor helped a student process her brother’s arrest the previous night.

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Above, the instructor demonstrated a genuine and holistic interest in his students’ well-being. He proactively reached out to each student to offer assistance instead of depending on his students to seek help. At the same time, and likely not unrelatedly, his students asked for help more frequently than typically observed. Lastly, when students struggled with content or on an assessment, the instructor broke down content to determine what students understood, providing alternative examples, scaffolding content, or affirming knowledge as needed. We discuss the merits of these strategies in greater detail below. Building trust to facilitate help-seeking. First and foremost, the students in credit recovery labs are individuals with agency and out-of-school lives. Acknowledging this reality, some teachers discussed in interviews the importance of taking on roles unrelated to the effective use of digital tools. One teacher explained, "I'm their administrator, counselor, and teacher." As a credit recovery program, the teacher explained that he counseled the students least engaged in school, which stemmed from a host of reasons. For instance, one student came into class crying on a Monday because her grandmother was shot over the previous weekend. The student needed to process this experience with her instructor before being ready to engage in instruction. Students sharing experiences such as these with their instructors were not uncommon. Unsurprisingly, we observed a significant, positive association between interaction ratings and instructors who took the time to build rapport with their students by demonstrating an interest in their lives (p = 0.008). Although at first glance, non-academically focused conversations might appear to distract from course progression, the research of Brion-Meisels (2015, 2016) indicated this might be an essential first step to earning students trust, encouraging both help-seeking and engagement, two critical components of success in online courses (Ahn, 2011).

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[Not] proceeding without instructor assistance. While many teachers relied on students to ask for help, some instructors identified students in need of assistance, often using technology-based resources. The availability of real-time progress and assessment information on each student helped instructors determine which students were actively interacting with the software, and of those students, which ones were struggling to master content. To monitor student engagement, one teacher used LanSchool (classroom management software), which allowed her to log into any of the students' desktops and see a screenshot of their desktop at that moment. Although only observed in 10 percent of whole-class observations, we found statistically significant associations between teachers’ use of these computer-based tools to monitor students and both classroom interaction (p=0.015) and instructor engagement ratings (p=0.023). The classroom teachers facilitating the online credit-recovery in this school district also had regular access to a broad array of data on student progress through the course system. For example, instructors could identify how far a student was progressing through a course, scores on quizzes and tests, and which questions students answered incorrectly. In the following example, instructors used the progress monitoring reports provided by the online course program to facilitate individualized conversations with each student about their progress. When students log in each class, they can see their progress. The instructor has a different screen to monitor where they are. Kids check in with her and set a goal for where they want to be, looking for six percent progress per week. The instructor tends to show students the resources they have to track their progress. While the above instructor used progress monitoring tools to develop goals and personal connection with students, other instructors used these tools to identify students requiring just-in-

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time assistance. In one such example, “The instructor was monitoring the student's progress, as he noticed the student's low quiz score and came over to discuss content with him and help him determine the correct answers.” Instead of providing more instructional assistance to the most assertive students or students that the instructors perceived as engaged, both above teachers used the information available through the online course system to identify students requiring assistance and drive one-on-one conversations with students in the classroom. While relying on student help-seeking may exacerbate existing advantage based on student access to and embodiment of middle-class behavioral norms, taking advantage of detailed data available to educators when students complete courses online, could help teachers facilitate individualize learning for all students, potentially providing more equitable access to instructors’ time and expertise. A similar result could be accomplished by regularly checking-in with all students, offering assistance instead of relying on students to seek it. Improving the quality of assistance received. The value of an instructors' time in an online credit recovery lab is based in part on their capacity to connect to students, but instructors must also be able to provide content-specific assistance in a format that students can comprehend (Brion-Meisels, 2015, 2016). Many credit recovery labs that provided the highest quality educational experiences for students supported students completing courses in a single subject, such as science or math, with a teacher certified in that subject area assigned to the lab (Taylor et al., 2016). For instance, the instructor described in the excerpt at the beginning of this section was a certified math and bilingual teacher with over a decade of experience teaching every math course from algebra one through calculus. It is unreasonable to expect an instructor without comparable subject and instructional expertise to provide the same caliber of assistance. Schools do their best to staff traditional courses with certified, experienced teachers; always, but

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particularly when students from underserved populations are disproportionately assigned to online credit-recovery courses, school should do the same to support equitable educational experiences in online courses (Hannum et al., 2008; Means et al., 2013; Taylor et al., 2016; Zhao et al., 2005). In this section, we described student-teacher interactions in a digital credit-recovery program across 18 schools in a large, urban district in the Midwest. Interactional spaces included the decision and process of instructors offering assistance or students asking for help and the type of support provided. The quantity and quality of these interactions mediated the extent to which students learned content, made course progression, or experienced demoralization, as summarized in Figure 1. Many of these interactional spaces aligned with mechanisms for class and race-based achievement gap reproduction identified in interpretive social reproduction scholarship conducted in traditional classroom settings. Where data permitted, we observed similar patterns of disparate access to quality educational opportunities within this predominately online, technology-driven educational environment. Below, we discuss in greater detail the research and practical implications of our findings, including opportunities for expanding the use of promising instructional strategies. Discussion This study extends current literature on spaces of educational inequality by mapping help-seeking interactions between students and teaching in online credit-recovery labs to spaces associated with disparities in academic opportunities and attainment in traditional classroom settings. Across our 156 classroom observations, few students enrolled in online credit-recovery courses had access to consistent, constructive interactions with the online interface delivering instruction. Most students, whether struggling with course content or demoralized and

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disengaged, required instructor support to obtain full access to the learning environment. Inequitable access to quality learning experiences in this context has profound inequity implications and importance for the overall quality of education in the district. Twenty percent of all course credits completed in the observed school district during the 2016-17 school year were earned online, with historically underserved populations disproportionately assigned to online courses. Recommendations While observations often demonstrated disparate access to quality educational experiences in online credit-recovery labs that mirrored those documented by others in traditional classroom settings (i.e., Calarco, 2011; Lareau, 2003; Streib, 2011), we identified several guidelines for policy and practice that may improve equitable educational access in online courses: ● Instructors should provide explicit expectations and proactive assistance to students, with students most likely ask for and accept help if instructors demonstrate trustworthiness and respect (Brion-Meisels, 2015, 2016). ● The use of technological tools and real-time data can facilitate student-teacher interactions, such as goal setting and targeted support. ● Low student-teacher ratios and assigning teachers certified in course subjects can enhance educational quality. More specifically, to improve student access to assistance, instructors could encourage help-seeking by explicitly communicating expectations (Delpit, 2006) and encouraging proactivity (Calarco, 2011). Expectations for instructors must also be clear. Instructors must do more than prevent behavioral disturbances and provide technical support. Instead of sitting

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behind a desk waiting for students to approach with requests for assistance, teachers should monitor the classroom, either physically or with software, to identify and seek out students requiring instructional or motivational support. Instructors should also be prepared to fill noninstructional roles as counselor or confidant to build trust and demonstrate respect, which improves the likelihood students ask for and accept instructional assistance (Brion-Meisels, 2015, 2016). Transformation of the role of the instructors in this manner will likely require professional development and the minimization of administrative demands. While many student-teacher interactions highlighted in our findings resulted in inequitable instructor assistance, other digital tools showed potential to reduce interactions identified by prior research as spaces that reproduced inequality. In online learning environments, instructors have access to real-time data on student progress, engagement, and learning, but the most effective use of this information requires training and practice. While not the sole means to identify students needing assistance, computer-assisted monitoring of students appeared to allow teachers to identify better students who were off-task and students who required instructional aid. These findings indicated increased use of computer-based tools might assist teachers with classroom management, enhancing the quantity and quality of instructional assistance. Instructors could use this available information to initiate conversations with students about their progress and deepen instructor understanding of student knowledge and engagement based on objective versus subjective measures. For instance, with clear expectations and support, the checking of quizzes before submission can be transformed into an opportunity to provide targeted instructional assistance through blended learning. From a structural standpoint, prioritizing low student-teacher ratios in credit recovery labs has the potential to increase the quantity and subsequent quality of student-teacher

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interactions. This is consistent with the work of Lazear (2001) who demonstrated that class size reductions would have the largest impact in classrooms serving students classified as disruptive, although teacher quality mediates that benefit. Similarly, assigning instructors with the content and teaching background to serve the instructional needs of students is necessary to ensure highquality learning experiences. At minimum, there appears to be a positive association between factors associated with student learning and teacher certification and a negative relationship between learning environment and instruction by a substitute teacher. Future Research Directions Future research should expand understanding of the factors students consider when asking for assistance in online classrooms, including pre-existing student-teacher relationships and the type of support an instructor may provide. Researchers with access to observations of the same student over time may be able to identify how students' academic behaviors, including help-seeking, change across repeated student-teacher interactions. In turn, there are equity implications based on the extent to which teachers’ perceptions of students inform the quantity and type of assistance volunteered, which we were not able to examine with our cross-sectional data. Perhaps most substantially, our data collection process prevented us from making claims about disparities by socioeconomic status or racial identities. Consequently, the assumption that differences observed in traditional classroom settings transfer entirely to online classrooms merits additional examination, while additional documentation on the extent to which differential student-teacher interactions by race or class characteristics mediate academic achievement and engagement would strengthen the motivation for future study.

__________________________________

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1

Although not a focus of this study, interactions between students and curriculum are equally important to learning opportunities. Frameworks such as critical multiculturalism (Ladson-Billings, 2004; McLaren, 1994) can help examine the ways in which these interactions reflect and reproduce normative narratives along race, class, and gender lines, among others. 2

The observed school district defined neighborhood schools as giving priority to students who live close to the school. Specialty schools focused on a program or area of study such as the arts or gifted and talented programming, while alternative schools targeted students requiring flexibility or attention, including students at-risk of dropping out.

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Ahn, J. (2011). Policy, technology, and practice in cyber charter schools: Framing the issues. Teachers College Record, 113(1), 1-26. Arnot, M., & Reay, D. (2007). A sociology of pedagogic voice: Power, inequality, and pupil consultation. Discourse, 28(3), 311-325. Altonji, J. G., & Pierret, C. R. (2001). Employer learning and statistical discrimination. The Quarterly Journal of Economics, 116(1), 313–350. Bernstein, B. (1975). Class and pedagogies: Visible and invisible. In Sadovnik, A. R. (Ed.), The Sociology of Education: A Critical Reader (2nd Volume). New York: Routledge. Bourdieu, P. (2010). The forms of capital. In Sadovnik, A. R. (Ed.). The sociology of education: A critical reader (2nd Edition). New York: Routledge. (Original work published 1986). Brion-Meisels, G. (2015). “Can I trust you with this?” Investigating middle school students’ use of learning supports. Urban Education, 50(6), 718-749. Brion-Meisels, G. (2016). “It starts out with little things”: An exploration of urban adolescents’ support-seeking strategies in the context of school. Teachers College Record, 118(1), 1-38. Calarco, J. M. (2011). “I need help!” Social class and children’s help-seeking in elementary school. American Sociological Review, 76(6), 862–882. Calarco, J. M. (2014). The inconsistent curriculum: Cultural tool kits and student interpretations of ambiguous expectations. Social Psychology Quarterly, 77(2), 185-209. Clements, M., Stafford, E., Pazzaglia, A. M., & Jacobs, P. (2015). Online course use in Iowa and Wisconsin public high schools: The results of two statewide surveys. Washington, DC: Regional Educational Laboratory Midwest. Delpit, L. (2006). Other people's children: Cultural conflict in the classroom. New York, NY: The New Press. Devine, P. G., Forscher, P. S., Austin, A. J., & Cox, W. T. (2012). Long-term reduction in implicit race bias: A prejudice habit-breaking intervention. Journal of Experimental Social Psychology, 48(6), 1267-1278. Downey, D. B., & Pribesh, S. (2004). Why race matters: Teachers’ evaluation of students classroom behavior. Sociology of Education, 77, 267–282.

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Ewens, M., Tomlin, B., & Wang, L. C. (2014). Statistical discrimination or prejudice? A large sample field experiment. Review of Economics and Statistics, 96(1), 119-134. Gardiner, W. (2014). Skepticism Over Rising Graduation Rates. Education Week, October 6. Gemin, B., Pape, L., Vashaw, L., Watson, J. (2015). Keeping pace with K-12 online learning: An annual review of policy and practice, 2015. Durango, CO: Evergreen Education Group. Hannum, W. H., Irvin, M. J., Lei, P. W., & Farmer, T. W. (2008). Effectiveness of using learner‐ centered principles on student retention in distance education courses in rural schools. Distance Education, 29(3), 211-229. Heath, S. B. (1982). Questioning at home and at school: A comparative study. In Spindler, G. (Ed.), Doing the Ethnography of Schooling: Educational Anthropology in Action. Prospect Heights, IL: Waveland Press. Ladson-Billings, G. (2004). New directions in multicultural education. In J. A. Banks & C. A. McGee Banks (Eds.), Handbook of Research on Multicultural Education (pp. 50-65). San Francisco, CA: Jossey-Bass. Lareau, A. (2003). Unequal childhoods: Class, race, and family life. Berkeley, CA: University of California Press. Lareau, A., & Calarco, J. M. (2012). Class, cultural capital, and institutions: The case of families and schools. In S. T. Fiske & H. R. Markus (Eds.), Facing social class: How societal rank influences interaction (pp. 61-86). New York: Russell Sage Foundation. Lareau, A., & Weininger, E. B. (2003). Cultural capital in educational research: A critical assessment. Theory and Society, 32(5-6), 567-606. Lazear, E. P. (2001). Educational production. The Quarterly Journal of Economics, 116(3), 777803. Luykx, A., Lee, O., Mahotiere, M., Lester, B., Hart, J., & Deaktor, R. (2007). Cultural and home language influence on children’s responses to science assessments. Teachers College Record, 109(4), 897-926. McLaren, P. (1994). Critical pedagogy, political agency, and the pragmatics of justice: The case of Lyotard. Educational Theory, 44(3), 319-340. Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1-47.

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Mehan, H. (1992). Understanding inequality in schools: The contribution of interpretive studies. Sociology of Education, 65(1), 1-20. Newman, R. S. (2000). Social influences on the development of children's adaptive help seeking: The role of parents, teachers, and peers. Developmental Review, 20(3), 350-404. Parks, A. N. (2010). Explicit versus implicit questioning: inviting all children to think mathematically. Teachers College Record, 112(7), 1871-1896. Patrick, H., Anderman, L. H., Ryan, A. M., Edelin, K. C., & Midgley, C. (2001). Teachers’ communication of goal orientations in four fifth-grade classrooms. The Elementary School Journal, 102(1), 35–58. Patrick, S., Kennedy, K., & Powell, A. (2013). Mean what you say: Defining and integrating personalized, blended and competency education. International Association for K-12 Online Learning. Peck, C., Hewitt, K. K., Mullen, C. A., Lashley, C. A., Eldridge, J. A., & Douglas, T. R. M. (2015). Digital youth in brick and mortar schools: Examining the complex interplay of students, technology, education, and change. Teachers College Record, 117(5). Queen, B., & Lewis, L. (2011). Distance education courses for public elementary and secondary school students, 2009-10. Washington, D. C.: Institute of Education Sciences. Rist, R. (1970). Student social class and teacher expectations: The self-fulfilling prophecy in ghetto education. Harvard Educational Review, 40(3), 411-451. Robinson, J. P., & Lubienski, S. T. (2011). The development of gender achievement gaps in mathematics and reading during elementary and middle school: Examining direct cognitive assessments and teacher ratings. American Educational Research Journal, 48(2), 268-302. Rumberger, R. W. (2004). Why students drop out of school. In G. Orfield (Ed.), Dropouts in America: Confronting the graduation rate crisis (pp. 131–156). Cambridge, MA: Harvard Education Press. Streib, J. (2011). Class reproduction by four year olds. Qualitative Sociology, 34(2), 337–352. Taylor, S., Clements, P., Heppen, J., Rickles, J., Sorensen, N., Walters, K., & Allensworth, E. (2016). Getting back on track: The role of in-person instructional support for students taking online credit recovery. Washington, D. C.: American Institutes for Research. Thevenot, B. & Butrymowicz, S. (2010). Can credit recovery courses cut dropout rates? The Texas Tribune.

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Torpey, E., & Watson, A. (2014). Education level and jobs: Opportunities by state. Washington D. C.: US Bureau of Labor Statistics. Van den Bergh, L., Denessen, E., Hornstra, L., Voeten, M., & Holland, R. W. (2010). The implicit prejudiced attitudes of teachers: Relations to teacher expectations and the ethnic achievement gap. American Educational Research Journal, 47(2), 497-527. Zhao, Y., Lei, J., Yan, B., Lai, C., & Tan, H. S. (2005). What makes the difference? A practical analysis of research on the effectiveness of distance education. Teachers College Record, 107(8), 1836. Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183.

MAPPING HELP-SEEKING Figure 1. Student Responses When Struggling with Content in Online-Credit Recovery Computer Labs

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Appendix A: Dimensions of Digital and Blended Instruction Rated in Observations The following dimensions of digital and blended instruction and the settings in which they are used are rated by the observation instrument we employ in this study. 









   

Physical environment: How and where students access the instructional setting, including the technological setting and any associated limitations, and who else in the same physical environment as the student could assist with technological problems and support learning; Technology and digital tools: How students access instruction, including internet connectivity, hardware and software in use, and the safety, operability and accessibility of the technology; Curricular content and structure: Content and skill focus, who developed it and where it is located (e.g., software loaded onto a tablet, paper workbook), stated learning objectives, sequence and structure, level of rigor or intellectual challenge, and ability to meet and adapt curricular content to student needs; Instructional model and tasks: Role of instructor and software in instruction (what drives instruction); purpose or target of instruction; student/instructor ratio and grouping patterns, multimodal instruction; order of thinking required and application of technology in instructional tasks, and ability to meet/adapt instructional model and tasks to student needs; Interaction: How much interaction with a live person, and does the technology affect the ability of the instructor or student to positively interact with one another and the instructional resources? Digital citizenship: Are students using the technology as intended by the instructor and/or instructional program? Student engagement: Overall student engagement levels, level of student self-regulation and persistence, and level of community within the instructional setting; Instructor engagement: Overall instructor engagement levels (passive or active) and instructor efforts to encourage engagement; Assessment/feedback: Who develops and manages the assessment (instructor, provider via software), structure, and whether it is individualized to student learning and relevant to stated learning goals.

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