Public Online Charter School Students

Abstract

There has been a steady growth of the K-12 student population taking courses online. This study examined reasons for students to choose a public online charter school program and their perceptions of online discussion. A survey was sent to 1,500 students newly enrolled in a statewide public online charter school program. From those who responded, 44% indicated that the online discussion component is not helpful in achieving their academic goals. Also, further analysis suggested that those who drop out of traditional schools probably would not stay even in an online program unless the program adequately supported the students. In this report, interrelationships among perceptual measures along with traits and preferences of online students are discussed and suggestions are made for educators.

Keywords

online education charter school noncognitive predictors dropouts

Introduction

Since 1997, when the Florida Virtual School started as the nation’s first online public high school, there has been a steady growth of the K-12 student population taking courses online (Fulton, 2002; Gray & Tucker, 2006; Picciano & Seaman, 2007) while more education services and competitors were created for the growing population seeking flexible education options (alternatives to brick-and-mortar public schools). Marsh, Carr-Chellman, and Sockman (2009) report that enrollment in this new form of education is increasing 20% annually. They also report that parents who choose the alternative education option for their children do so because of individually catered program options and daily progress reports that are transparent to students, parents, and educators. Overall, it seems that alternative education options around online education are expected to evolve and expand continuously, if not increasingly causing substantial changes in the entire education system.

Nonetheless, the term online education has been broadly and interchangeably used. Also, elements that possibly make up an online education program could be varied by providers. Particularly, when it comes to defining what online education may be, there are numerous definitions or even misconceptions (Clark, 2008; Pena-Shaff, Altman, & Stephenson, 2005; Rovai & Barnum, 2003) among students (and parents) who consider an online education program as an alternative option and even educators who discuss online learning activities through academic forums (Picciano, 2002).

With rapidly advancing information and communication technology options, there has been a continuous evolution of learning technology for learners. At the same time, the labels used to describe the various forms of education have also changed (e.g., Internet-based learning, networked classroom, virtual learning, cyber school, e-learning, Web-based distance learning, etc.) around the concept of learning online (Ally, 2004; Anohina, 2005). Amid discussions on what might determine or constitute online learning (e.g., downloading or reading content online, submitting assignments through Internet for self-study, using webinars as virtual classrooms, interacting with learning stimuli such as simulated systems or virtual personas, etc.), a substantial number of studies stressed the importance of learner interaction (i.e., active discussions or interactions with facilitators or peers in the process of learning) as one of the most critical aspects of online learning (Bray, Aoki, & Dlugosh, 2008; Cavanaugh, 2001; Murphy & Coffin, 2003; Reio & Crim, 2006; Swan, 2002).

However, most interestingly, students (or parents) who consider online education do not necessarily understand what may be actually required in online learning, and many often choose online learning because of the notion of flexibility in time, space, or pace over the traditional brick-and-mortar schools (Berge & Clark, 2006). As a result, many students learn about online learning after signing up and going through the actual program. In addition, students who are starting in an online program often discover that online learning in fact requires much more active participation and a much higher level of self-regulation along with support often from family or significant others, especially in the early stage. Importantly, what they experience in the very early stage of online learning seems to affect their later perception, attitude, satisfaction (Pillay, Irving, & McCrindle, 2006; Waschull, 2005), and also decision on whether to continue or drop out. One might be easily persuaded to believe that students choose online learning simply because they like to learn online or want to participate in online interactions (i.e., mistakenly taking it for granted), but the reality might be that they may not know enough about online learning or may not even like what is considered to be a critical component of online learning: a high level of self-regulation, active interaction, or multi-channel online discussion.

While a substantial number of research studies have focused on issues related to online school administration, management, or teacher competency development (Berge & Clark, 2006), little has been done in investigating the early stage (i.e., perhaps the most critical period) of student learning online and especially what they might think about online interaction or discussion experiences. To date, it is not easy to find an empirical study explaining how online students enrolled in public K-12 online school programs perceive online discussions (i.e., a rudimentary inquiry, yet a critical premise for online program design and evaluation). In addition, existing literature discusses potential factors leading to student success or satisfaction in general (e.g., Bernard et al., 2004; Cooze & Barbour, 2007; Wang & Newlin, 2000; Waschull, 2005), but there is a dearth of literature investigating K-12 online students’ perception of online discussion, especially in the critical early stage. Such deficiency of empirical studies leaves us to question possible reasons behind their early perception on the practice of online discussion. For example, if students find the practice of participating in online discussions helpful, is it because they are more academically inclined? Or is it because they are more attuned to the social interaction aspect of online learning? In contrast, if they do not find online discussions helpful in general, is it because they are better managers of their own learning (i.e., exercise more self-regulation)? Or is it because they want to study alone? How do you construct an online learning environment for those who would like to learn alone without online team interactions or discussions? Thus, it seems important and timely to help develop an empirical foundation on basic inquiries regarding students’ perceptions of online discussions and also subsequent inquiries on possible reasons for such perceptions by investigating relevant variables and interrelationships among them. For example, as identified in earlier studies (e.g., Roblyer & Davis, 2008; Waschull, 2005), variables such as previous experience, self-regulatory dispositions, or interest in learning certain subjects online (i.e., linked to self-efficacy) seem crucial to our inquiry.

In short, the central issue of this exploratory study is the importance of understanding student perceptions when selecting modes of learning. Determining student perceptions (and potential causes or explanatory reasons behind them) in the early stage of online learning should be beneficial for educators who want to better understand the target population and then tailor learning experiences in ways that can best contribute to positive student learning outcomes. We intentionally limit our scope of discussion in the present report to the findings on students’ perceptions (i.e., helpfulness in learning) of online discussion, particularly in the early stage of enrollment, and lay the necessary empirical foundation that can later be linked to additional variables involving attrition, satisfaction, and academic achievement in follow-up longitudinal investigations.

Literature Review

School Choices in the K-12 Education System

With the advancement in information and communication technologies and the increased level of interest (i.e., from the private, public) in school choices, we have witnessed a substantial growth of online schools or programs in recent years. Such a rise of alternative school choices provides expanded access and flexible learning opportunities for students (Clark & Berge, 2005). In parallel, many of the original distance learning programs that used to take place through correspondence schools or home schooling systems have also been transformed to online learning programs (Bates & Bates, 2005). Today, across the country, approximately three-fourths of U.S. schools provide students with either fully online or blended (combination of on- and off-line) courses, with 69% of those students enrolled at the high school level (Picciano & Seaman, 2007). Recently, Picciano and Seaman (2009) indicated that online learning at the K-12 level has experienced a substantial enrollment increase in a short period, yet it is still “in its nascent stages” (p. 22).

Another increasingly noticeable phenomenon in the recent decade stems from the charter school movement (Shober, Manna, & Witte, 2006). Charter schools are public (i.e., mostly brick and mortar) schools that operate under a contract or charter from an authorized agency dealing with public education systems. In general, most charter school educators have more freedom to try new forms of teaching or innovative curricula, and school administrators can experiment with nonconventional management and operation strategies. However, they are still responsible to maintain respective academic standards and certainly accountable for student achievement test results while they are upholding their charter (Bulkley & Fisler, 2002). Overall, the number of charter schools and charter school enrollments continue to grow each year (Zimmer & Zimmer, 2009) despite numerous challenges and controversies. A recent report on the national trends of charter school enrollment and demographics shows that there were over 1.5 million students enrolled in charter schools throughout the United States in 2009 (Allen, Consoletti, & Kerwin, 2009).

By combining the distinctive features of online schools and charter schools, online charter schools provide K-12 learners with access to expanded school choices and often more flexible and diverse academic programs (Barbour & Reeves, 2009; Bogden, 2003; McCluskey, 2002). Typically, online charter schools are independent public schools of choice that offer K-12 courses (i.e., prepackaged curricular coupled with learning software and hardware) through Internet-based methods with time or distance separating the teacher and learner (Clark, 2001). Most online charter schools are full-time, statewide, and asynchronous, with students learning from home and teachers working out of a school building or home office (Greenway & Vanourek, 2006). Courses can be scheduled to be completed during a common academic timeframe (e.g., a standard school semester) or be self-paced, with students completing a course when content mastery has been achieved (Fulton & Kober, 2002). Online charter schools, with sponsorship from local or state educational organizations, often benefit from operational flexibility and their status as independent public schools. Consequently, online charter schools possess the ability to serve a wider range of students (i.e., as compared to brick-and-mortar schools).

Online Discussion and Active Participation

Interaction or discussion has been argued to be the fundamental process of learning (Wenger, 1998) and also one of the key challenges in online learning environments (Bento & Schuster, 2003). Researchers’ views on “participation” in online learning may be conceptually varied (Davies & Graff, 2005; Vonderwell & Zachariah, 2005), but they still seem to at least agree on the importance of interactions in online learning environments. Thus, active online participation is often viewed as a pivotal factor for student success, satisfaction, and persistence in online learning (Bray et al., 2008; Reio & Crim, 2006). In a relevant vein, Swan (2002) reported that online discussion was one of the most significant features of online learning identified by 1,406 college students who participated in her study. Similarly, Richardson and Swan (2003) find that online discussion is one of the activities most beneficial to their learning. The review by Rice (2006) on online learning also presents the important relationship between online learning and online discussion.

In general, interactivity in learning processes enhances knowledge acquisition and engenders positive attitudes to learning while supporting the social needs of the participants (Parker, 1999). Furthermore, studies on postsecondary online education report a significant linkage between student satisfaction and the quality and quantity of interactions (Moskal, Dziuban, Upchurch, Hartman, & Truman, 2006; Shea, Fredericksen, Pickett, Pelz, & Swan, 2001). However, studies on K-12 students’ perception of the role of online discussion are quite limited. One of the noteworthy exceptions is a study by Barbour, McLaren, and Zhang (2008), which examined the student perceptions of the various components of online programs for secondary school students. In the study, students state the lack of the sense of an online community, due to asynchronous interactions. Nonetheless, they largely enjoyed their online program and the ability it offered to control their own learning. Similarly, Tunison and Noonan (2001) also examined student perceptions of an online discussion and found that the students recognized the value of the asynchronous discussion and its potential positive effects, but that the student did not actively participate in online discussion, which limited their effectiveness.

In sum, online discussion may be considered one of the most important components of online learning, potentially compensating for the lack of real-time discussions and interactions that are typical in brick-and-mortar classrooms. As such, understanding how students view online discussions (e.g., helpfulness in online learning) seems to be one of the important criteria for predicting their later engagement, attitude, satisfaction, or achievement. Interestingly, it is possible that not all K-12 students who are considering online schools may fully understand what it means to be online students or what online discussion may entail until they participate in the process. Furthermore, not all online students may enjoy learning with peers or through online discussions.

Potential Factors Influencing Online Students

There is a lack of empirical studies on K-12 online students, which makes it difficult to draw meaningful conclusions about the factors that will best predict student engagement and learning outcomes. Nonetheless, there are two noteworthy themes found from a meta-analysis study on online learning programs by Means, Toyama, Murphy, Bakia, and Jones (2009). The first theme deals with promoting reflection and self-monitoring and the second deals with individualizing the learner content based on student responses. Among numerous variables examined in the meta-analysis, the two identified themes seem more crucially related to the outcomes than others. Consequently, the two themes suggest that how individual students perceive or respond to managing their own learning and their learning experiences may be more important than whether there are certain features (e.g., simulations, guiding scripts, moderators, or quizzes) in the learning environment. In the higher education online learning setting, previous studies shed some light on the various types of traits and characteristics for further planning of learning programs that may lead to student success and satisfaction (Rovai & Barnum, 2003). However, there is no empirical study to date involving these constructs as potential predictors in the K-12 online school setting. Accordingly, we intend to focus on a series of perceptions that students may come with and begin to establish when they start in the online program (e.g., prior learning experience, perception on traditional schooling and materials, personal traits, online learning subjects, etc.). Therefore, we briefly review constructs that have been examined in previous online education research or believed to be related to perceptions or personal traits of online learners.

Noncognitive traits

Heckman and Rubinstein (2001) defined traits as patterns of thoughts, feelings, and behaviors that are linked to human motivation, values, interests, and attitudes. Furthermore, they argued that identifying individual noncognitive traits might help predict how people actually think, perceive, act, or decide. Noncognitive traits include perseverance, motivation, self-control, and other aspects of conscientiousness (see Borghans, Duckworth, Heckman, & Weel, 2008). In the same vein, Farkas (2003) and Cunha, Heckman, and Schennach (2010) stress the importance of understanding the noncognitive traits of young children and their later potential impact on schooling and other outcomes in general. More specifically, noncognitive attributes seem to play an important role in reversing or limiting delays or deficiencies in cognitive development and academic achievement (Rosen, Glennie, Dalton, Lennon, & Bozick, 2010).

Long-term goal setting and task management

Long-term goal-setting ability is a key factor in self-regulation (Latham & Locke, 1991; Zimmerman, Greenberg, & Weinstein, 1994). Students with higher goal-setting ability may demonstrate higher self-efficacy and also self-regulation skills, which certainly affect their performance in school. Accordingly, several studies have reported the significance of goal-setting skills in students’ success in online learning (Moskal et al., 2006; Roblyer & Davis, 2008; Ronsisvalle & Watkins, 2005; Waschull, 2005), and this skill has been identified as an important factor in determining students’ satisfaction and performance in online learning environments (Bolliger, 2004; Bray et al., 2008; Reinhart & Schneider, 2001). Obviously, students’ ability to manage their own learning (i.e., learning path, pace, and task planning) is a critical factor for success (Waschull, 2005). With much more freedom and flexibility presented in online learning, students’ higher levels of commitment, motivation, and sense of responsibility would be evidently crucial in online learning. In other words, to keep up with the class all by themselves, students have to plan their own tasks efficiently and complete all assignments on time (Berge & Clark, 2006) and often in long terms (e.g., semester long). If students can better manage their study time and their pace of learning, they will be better able to balance their life demands and social activities as well. Zimmerman et al. (1994) and Kozma et al. (2000) argue that task-planning ability affects learners’ achievement and satisfaction in online learning environments and can be a good predictor of their academic success (Roblyer & Davis, 2008).

Multitasking

Delbridge (2000) defined multitasking as “accomplishing multiple-task goals in the same general time period by engaging in frequent switches between individual tasks” (p. 1). Generally, multitasking is viewed as a trait and ability of a new generation. Definitions may vary, but listening to an iPod, chatting with multiple parties via instant messaging, and texting on a mobile phone all while working on problems on a homework website would be (although extreme) an example of a multitasking scenario. The belief that online high school students are master multitaskers (Barnes, Marateo, & Ferris, 2007; Wallis, 2006) has been investigated in a few studies, and contradictory results have been reported (see Hembrooke & Gay, 2003; Just, Keller, & Cynkar, 2008). Besides, splitting attention and switching through multiple cognition channels do not seem to allow young students with sufficient cognitive resources to solve critical thinking problems (Oblinger & Oblinger, 2005). Although the kinds and implications of multitasking in emerging academic environments (i.e., especially online learning with multimodal stimuli) need to be investigated in depth, at least the general consensus is that learning while multitasking does not mean higher-quality learning is taking place (Dzubak, 2008; Kolikant, 2010).

Group work

In the earlier days of the online learning era, Kerka (1996) warned that the physical separation of online learners from their peers and the course instructor could reduce the sense of community among online learners and it might raise feelings of disconnection. Cavanaugh, Gillan, Kromrey, Hess, and Blomeyer (2004) also share the same view in that such physical detachment could possibly affect students’ learning and the overall process of knowledge construction and acquisition in online learning. However, Watson (2007) disagrees with the earlier views while Barbour and Plough (2009) argue that students enrolled in K-12 online charter schools could overcome such limitations (e.g., physical isolation from peers and instructor). Accordingly, several best practice possibilities (e.g., integration of social networking tools, creation of online communities, promoting online team collaboration, etc.) have been suggested (see Alexander, 2006).

Schooling structure

While some students decide to join online courses to accelerate or slow down their learning pace or seek more freedom (Ferdig, DiPietro, & Papanastasiou, 2005; Setzer & Lewis, 2005; Waits, Setzer, & Lewis, 2005), others choose online courses due to various difficulties with traditional school settings (Kozma et al., 2000). The challenges may include suspension, expulsion, medical treatment, constant relocation or traveling, extreme boredom, being bullied or left behind, school violence, or young parenthood. Also, those students who find traditional schooling difficult or are at risk of failing often consider online schools (Rice, 2006; Ronsisvalle & Watkins, 2005).

Subject preference

Hannum, Irvin, Banks, and Farmer (2009) report that the most favorable courses among K-12 online learners are foreign language, algebra, psychology, sociology, composition, and U.S. history. In this regard, Cavanaugh et al. (2004) assert that subject areas do not seem to matter in students’ outcomes in online high school courses, but literacy skills can inhibit student learning and therefore influence which topics are favored and ultimately chosen. However, it has been suggested (e.g., see Bulkley & Fisler, 2002) that further longitudinal data be collected to provide empirical comparisons between different sets of variables including school settings, subject preferences, and later achievements in particular course subjects.

Prior experience

Students’ prior online learning experiences have also been presented as one of the factors that affects students’ later attitudes and perceptions. Such prior experiences correlate to their later successes and satisfaction in online programs (Pillay et al., 2006; Waschull, 2005). In this regard, Cavanaugh et al. (2004) specifically listed influential factors that might arise from prior experience and they include: number of online learning sessions, duration, pacing, timing, and type of interactions. Overall, investigating students’ prior experience is beneficial in predicting their later attitude and perception of online settings (Pillay et al., 2006; Waschull, 2005).

Method

Participants

To investigate interrelationships of some of the aforementioned potential factors that seem to be associated with perceptions on online discussion, a link to an online survey was sent to 1,500 students newly enrolled in a statewide public online charter school program in the United States in the beginning of the 2009 academic year, approximately 14 days after the start date. The students were enrolled in 9th- through 12th-grade classes. Their ages ranged from 13 to 22 years old. While responding to the survey, the students were able to ask the learning advisor assigned to each student in the program about any of the survey questions and obtain clarifications if needed. A total of 524 students responded to the survey within 2 weeks (i.e., closed afterwards), and 459 responses (i.e., after omitting substantially incomplete responses) were used in the analysis. For adequate statistical power¹ needed for our analyses such as structural equation modeling (SEM), we sought to have more than 400 participants. Therefore, the sample size seemed adequate for achieving the intended precision of parameters (see Weston & Gore, 2006, for discussions on sample sizes for SEM).

Survey

The survey consisted of 26 questions with 4-point Likert-type scale responses (e.g., disagree completely = 1, somewhat disagree = 2, somewhat agree = 3, completely agree = 4). The survey included learning preferences (i.e., preferences for group work, conventional schooling experience, conventional learning materials, and perceived helpfulness of online discussion), subject preferences (science, literature, social sciences, mathematics, technology), experiences as online students in the online charter school program, and personal traits and self-regulatory dispositions (i.e., multitasking, task planning, long-term goal setting). The items were derived largely from previous studies (see Cavalier, Klein, & Cavalier, 1995; ChanLin & Chan, 2004; Foehr, 2006; Li, 2007; Saunders & Saunders, 2002). The reliability of the items was measured with 114 sample responses; the Cronbach’s alpha scores (see Appendix 1 in the online version of the journal) of the items ranged from .85 (the lowest) to .92 (the highest).

Online Learning Environment

The online learning program consisted of a Web-based learning management system, learning advisors, subject teachers, and on-call intervention teachers. Intervention teachers take turns staying online every day from 6AM until 9PM to provide students with real-time interactions and to help those who might require help on assignments or projects.

The learning advisors provide general counseling on academic matters such as scheduling and online learning resources, while subject matter teachers manage curriculum development, assessments, and online delivery. The on-call intervention teachers respond to student inquiries via real-time textual chatting (synchronous), webinar, or telephone. The Web-based learning management system included features such as online subject textbooks, multimedia diagrams, animations, external on-demand video links (e.g., Discovery Channel, National Geographic, NASA, etc.), discussion boards (i.e., asynchronous threaded discussions), a virtual simulation lab, a virtual social lounge, quiz bank, messaging center, assignment tracker, help center, and learning progress and analytical reports, and so on. The discussion board, which is shared with others enrolled in the same course, was used by students to post questions (i.e., while completing scheduled daily assignments) for teachers to respond asynchronously. However, the discussion board was not a primary means of communication (i.e., somewhat distinguishable from higher education online programs that often use an asynchronous discussion board as a primary teaching tool).

The learning management system and its features were used universally across all subjects in the program. However, the participating students were not systematically tracked to see if they were using all interaction features or visiting all learning aid components while completing an assignment in a course. Such a tracking feature was not fully available at the time of the study.

Analysis and Results

Student Perceptions on Online Discussions

An evident dichotomy was noticed in the students’ perceptions of the quality and usefulness of online discussions. Of the 451 respondents, 252 (56%) indicated that the online discussion component in online learning is helpful in achieving their academic goals (i.e., somewhat agree or completely agree) while 199 students indicated otherwise (i.e., somewhat or completely disagree).

In order to find possible explanations for their perceptions of online discussions, we classified them into two groups, namely Group A (those for whom online discussion is helpful) and Group B (those for whom online discussion is not helpful) and investigated what factors affected their perceptions of online discussions.

Differences Between the Two Groups

Table 1 presents mean differences on various measures between those who find online discussion helpful and those who do not. The analyzed measures were: prior online learning experience (i.e., higher score indicates more prior online learning experience), working in groups (i.e., higher score means higher preference in working in groups), difficulty with traditional materials (i.e., higher score means more difficulty with traditional textbook-centered learning experience), difficulty with traditional schooling (i.e., higher score indicates more difficulty with conventional brick-and-mortar schooling experience), multitasking (i.e., higher score indicates learning while engaged in multiple activities), task planning (i.e., higher score indicates higher likelihood of planning daily tasks), and long-term goal setting (i.e., higher score indicates higher likelihood of setting long-term goals).

Table 1

Analysis of Group Mean Differences

Groups	Mean	Standard Deviation	Standard Error
Group A—Online discussion is helpful
Prior online learning experience	1.60	.907	.057
Working in groups	3.02	.818	.052
Difficult with traditional materials	2.36	.883	.052
Difficulty with traditional schooling	3.51	.653	.041
Multitasking	3.03	1.06	.067
Task planning	2.32	1.12	.070
Long-term goal setting	2.94	.926	.058
Group B—Online discussion is not helpful
Prior online learning experience	1.45	.845	.060
Working in groups	2.65	.946	.067
Difficult with traditional materials	2.04	.994	.070
Difficulty with traditional schooling	3.25	.796	.057
Multitasking	3.02	1.09	.078
Task planning	2.10	1.08	.077
Long-term goal setting	2.55	.952	.067

In all measures, the means of Group A are higher than those of Group B. Students in Group A indicate that they are more likely to have prior online learning experience, enjoy working in groups, do not like traditional school learning materials, do not like traditional schooling experiences, engage in multiple tasks while studying, make plans to complete tasks, and periodically set goals for long-term projects.

In order to investigate if the mean differences are statistically significant, ANOVA (analysis of variance) was performed and the results are shown in Table 2.

Table 2

Significance of Mean Differences

Measures	Mean Difference	F	p
Prior online learning experience	.151	3.271	.071
Working in groups	.376	20.398	.000**
Difficult with traditional materials	.317	12.809	.000**
Difficulty with traditional schooling	.264	14.982	.000**
Multitasking	.013	.015	.901
Task planning	.222	4.508	.034*
Long-term goal setting	.397	19.916	.000**

p < .05. **p < .001.

The mean difference in prior online learning experience was not statistically significant between those who find online discussion helpful and those who do not. In terms of the measure on working in groups, those who find online discussion helpful are the ones who also like to work in groups and the result was statistically significant at p < .001. In regards to traditional school materials, the difference was significant at p < .001, indicating that those who choose online learning and find online discussion helpful are the ones who did not like traditional school learning materials. The same is true for schooling experience as well. Those who choose online learning are the ones who apparently do not like a traditional schooling model. The mean difference on multitasking was not statistically significant. However, for self-regulatory dispositions such as task planning and long-term goal setting, those who find online discussions helpful are the ones likely to engage in more self-regulative practices and the results are statistically significant.

Analysis of Perceptions on Academic Subjects

In order to investigate if there is a different of perception on academic subjects between Group A and Group B, students’ responses on five major subject groups were analyzed. With all five subject groups, Group A (i.e., those who indicated online discussion is helpful) also indicated that they are more likely to enjoy learning the subjects online than Group B as shown in Table 3.

Table 3

Analysis on Academic Subjects

Groups	Mean	Standard Deviation	Standard Error Mean
Group A—Online discussion is helpful
Science	2.98	.874	.055
Literature	2.62	1.005	.063
Math	2.69	1.078	.068
Social science	2.91	.932	.059
Technology	2.96	.923	.058
Group B—Online discussion is not helpful
Science	2.80	1.005	.071
Literature	2.41	1.059	.075
Math	2.39	1.071	.076
Social science	2.75	1.061	.075
Technology	2.58	1.006	.071

Table 4 shows mean differences in perceptions of the five major subjects between Group A and Group B. Except for social science, students who perceive online discussion helpful indicated that they are more likely to enjoy learning science, math, literature, and technology online. The mean differences on the four subjects (i.e., science, literature, math, and technology) are statistically significant.

Table 4

Significance of Mean Differences on Academic Subjects

Subjects	Mean Difference	F	p
Science	.185	4.366	.037*
Literature	.203	4.324	.038*
Math	.308	9.102	.003*
Social science	.159	2.859	.092
Technology	.377	17.177	.000**

p < .05. **p < .001.

Structural Equation Modeling

The analysis of the student perceptions of online discussion and noncognitive trait measures, including preferences of five major subjects, indicates noticeable differences between those who find online discussion helpful and those who do not. However, it is not clear which variable is most influential in determining student perceptions of online discussion. Also, it is not apparent how possible reciprocal influences among the variables might contribute to the overall relationships and possibly help us understand potential logical paths between variables (e.g., presenting direct or mediated effects). Therefore, a structural equation model was employed as shown in Figure 1. In the model, the noncognitive traits (i.e., prior online learning experience, long-term goal setting, difficulty with traditional materials, difficulty with traditional schooling structure, and preference for group work) were assumed to influence student perceptions of online discussions to a large extent (i.e., as suggested by the analysis of variance). Also, online discussion was assumed to have positive correlations with the five subjects while long-term goal setting and task planning are also predicted to influence the subjects at different levels.

Figure 1.

Structural equation modeling.

The values on the paths are standardized coefficients. A path coefficient is a standardized regression coefficient (β) showing the direct effect of an independent variable on a dependent variable in the path model. Therefore, when the structural equation modeling involves multiple causal variables, path coefficients are partial regression coefficients that present the extent of effect of a variable on another in the path model controlling for other prior variables, using standardized data or a correlation matrix as input (Garson, 2008). The analysis showed that the incremental fit index (IFI) is .926 (i.e., the closer the index is to 1, the better fit the model is), Comparative Fit Index (CFI) is .919, and the normalized fit index (NFI) is .853.

Potential Factors Influencing the Perception on Online Discussion

In the model, preference for working in groups is associated with the highest correlation with preference for online discussion (β = .23, p < .001) among five potential factors examined. However, prior online learning experience (β = .17, p < .001), difficulty with traditional materials (β = .17, p < .001), and long-term goal setting (β = .17, p < .001) all presented equally high correlations. Difficulty with traditional schooling structure (β = .10, p < .05) presented the least significant association yet is nonetheless positively correlated to online discussion. Therefore, the model suggests that among the five exogenous variables, preference for working in groups might be the most significant predictor for the perceptions of online discussions. In other words, those who are more likely to favor working in groups would choose to learn online and are also more likely to perceive online discussions as helpful in online learning. In addition, findings also suggest that students who have more prior online learning experience, those who perceive traditional materials difficult, and those who tend to set long-term goals find online discussion more helpful. The same is true for those who perceive traditional schooling structure difficult or less congruous with their personal learning style (e.g., pace, path, media, location, etc.).

Correlations Between Online Discussion and Course Subjects

There are positive correlation coefficients between online discussion and all subject groups, indicating that the more helpful they find online discussion in online learning, the more likely they are to enjoy learning the subjects in their online learning program. In this regard, the correlations are significant between online discussion and mathematics (β = .15, p < .05), science (β = .13, p < .05), and technology (β = .10, p < .05). It is certainly worthy to note that significant relationships exist with math, science, and technology but not with social science or literature.

Correlations Between Self-Regulatory Dispositions and Subjects

The obvious reciprocal relationship between long-term goal setting and task planning indicated the highest significant correlation (β = .44, p < .001) in the model. Since long-term goals would not be effectively achieved without proper task and sub-task planning while task planning would be of no use if there were no goal in mind, the significant coefficient found in the reciprocal relationship in the model was not surprising. Nonetheless, in the construction of the model, the self-regulatory practices were believed to be influencing subject matters and the perception of online discussion differently. Overall, both long-term goal setting and task planning seem to positively influence most subject matters, suggesting those with long-term goals and those who are more likely to engage in task planning would also more likely enjoy learning the particular subjects online (i.e., based on the positive path coefficients found). However, only task planning’s influence on mathematics (β = .16, p < .001) and science (β = .11, p < .05) was statistically significant while none of the coefficients from long-term goal setting were statistically significant.

Discussion

The Dichotomy Phenomenon

The evident dichotomy between those who perceive the online discussion component helpful (56%) and those who do not (44%) in pursuing their academic goals online is a complex phenomenon to grasp. It has been suggested that students often choose online learning because of the notion of flexibility in time, space, or pace over the traditional brick-and-mortar schools (Berge & Clark, 2006). However, no study has suggested that students necessarily choose online learning solely because of the online discussion possibility, a component that is generally considered critical to the aspect of online learning (see Bray et al., 2008; Cavanaugh, 2001; Reio & Crim, 2006; Swan, 2002).

In fact, there are a few distinctive characteristics of online education that seem to attract and trigger traditional students to join an online program. For example, the nature and aspect of hyperlinked, often highly interactive, and nonlinear learning media presented in common online education programs are argued to be attraction factors for students to join online learning (Khalifa & Lam, 2002; Najjar, 1996). Also, students have been found to join online schools because the kinds of learning topics they wanted were not available in traditional schools (Kozma et al., 2000; Waits et al., 2005; Watson, 2007). Nonetheless, no study has shown that students seek online learning in order to engage in online discussion, regardless of the way it is presented (i.e., synchronous or asynchronous, moderated or not moderated).

From the distinctive dichotomy observed in our study, it is reasonable to affirm that not all students would consider online discussion as a necessary component prior to joining an online program nor would they find online discussion helpful after experiencing the program. Therefore, how we might equally and effectively accommodate the diverse set of online student groups seems to be of utmost importance in designing and operating online learning programs.

Also, regarding those who choose an online learning program as an alternative education option (i.e., most likely as a result of their negative perceptions on traditional schooling structure and conventional learning materials), it seems worthwhile to further pinpoint what specific aspects of traditional schooling or conventional media they had difficulties with. By understanding the specific reasons, educators would be able to devise educational programs that can best accommodate students with diverse learning preferences and styles. Overall, the discussion should not be on whether educators should provide brick and mortar schools or online schools, but how we might best take advantage of all learning modes and media for students with different preferences and needs.

Students in “Red Zone”

It is important to note that out of all criteria, “difficulty with traditional schooling” presented the highest mean score (3.25/4) for those who do not find online discussion helpful.² This suggests that these students did not like traditional schooling and therefore they chose an online learning program. However, they do not perceive the online discussion component helpful. They also do not enjoy learning math, science, literature, or technology as much as the counter group. Certainly, these are the prime candidates (thus in the “red zone”) who might not continue in the online program unless the program somehow accommodates their learning needs and styles accordingly. In addition, it seems quite important to note that these students are not simply indicating that they just do not happen to like the online discussion feature in the particular program or the type of discussion (e.g., asynchronous/synchronous or moderated/not moderated). These are the students who are not likely to set long-term goals or make plans to manage their tasks. Among all mean differences found between Group A and B, “long-term goal setting” showed the highest mean difference, indicating Group B students are significantly (p < .0001) less likely to be presenting self-regulatory dispositions. As it was pointed out earlier, the long-term goal-setting ability is a key factor in self-regulation (Latham & Locke, 1991; Zimmerman et al., 1994) and self-monitoring, which are essential when students pursue online learning as individuals (Means et al., 2009).

Therefore, online programs would need to accommodate extra academic counseling support for these students (i.e., especially help with long-term goal setting and task planning). A longitudinal follow-up study on this group of students—especially one reporting on course completion, course enrollment rates, drop-out rates, and academic performance—would provide valuable data toward validating these assertions.

It is clear that those who drop out of traditional schools because they do not like traditional school settings or materials (see Bridgeland, DiIulio, Morison, & Hart, 2006) probably would not stay even in an online program unless the program adequately supports and motivates them. Therefore, conducting a comprehensive aptitude assessment in the early stage of the program would be crucial, and such assessment should cover various cognitive and noncognitive measures. Noncognitive traits and behaviors seem to be as important as—or even more important than—cognitive skills in determining academic outcomes (see Heckman, Stixrud, & Urzua, 2006). Only by conducting such comprehensive assessment in the early stage will the subsequent program and online learning activities be adequately devised to best help the students. As reported by Marsh et al. (2009), parents chose online charter schools because of the daily progress bar, which helped the parents understand how their children were doing everyday in their online school while constantly challenging and motivating the students to meet or exceed daily goals. From the beginning of the program to the end, a daily individual assessment capability seems to be one of the best advantages of online learning programs today. It is possible for such features to integrate tools that are linked to task planning and monitoring (e.g., perhaps including e-mail or mobile messages for task reminders or task completion updates of their peers to encourage positive competition), particularly for those who begin to present low levels of motivation or self-regulatory practices. The evaluation of the effectiveness of such new online learning environment will be highly intriguing and require much research.

In terms of additional support for the students in Group B, other features such as moderated discussions at fixed schedules or individualized synchronous video sessions would be also interesting and would require further studies. However, Means et al. (2009) reminds us that the addition of synchronous communication with peers is not a significant moderator of online learning effectiveness. Further, they report that programs offering or encouraging online learning often suggest the use of authoritative figures as moderators, but research reporting the effects of this practice on student learning is mixed, while the majority of research to date has found that different learning media features do not always substantially affect learning outcomes.

Academically Inclined Online Students

In regards to the overall significant mean differences found on noncognitive trait measures, the mean of the group who indicated online discussion is helpful also indicated that they are more likely to have prior online learning experience, enjoy working in groups, do not like traditional school experience, do not like traditional learning materials, make plans to complete tasks, and periodically set goals for long-term projects and life-long objectives. The SEM model also confirms the significant correlations particularly between online discussion and difficulty with traditional schooling, difficulty with traditional materials, prior online learning, and working in groups.

Therefore, the findings suggest that those students who choose online learning do so because they do not like traditional schooling structure and conventional learning materials. Also, the decision to join the online program might have been influenced in part by their long-term goals (i.e., to complete a high school diploma online). These findings certainly help us understand the plausible reasons for them to choose the online program for their education. It is probably safe to affirm that these are the students who are in the “green zone” and would likely self-maintain a higher level of motivation. As reviewed earlier, a higher level of goal setting and task planning has been found to have a positive correlation with a higher level of motivation, satisfaction, and academic success (Kozma et al., 2000; Locke, Latham, Smith, Wood, & Bandura, 1990; Roblyer & Davis, 2008; Seijts & Latham, 2001). In addition, task-planning and goal-setting abilities are also found to be correlated with self-discipline and time commitment (Waschull, 2005). Students in Group A indicated that online discussion is helpful probably because they are more likely to make use of all learning resources and actively participate in online discussion while eagerly seeking help and finding answers to problems they are working on. These academically inclined students would probably use online discussion regardless of its mode or type (e.g., synchronous or asynchronous).

Overall, these students seem to be highly motivated and like interacting in groups online. Interestingly, this finding is incongruent to previous studies that indicated online learners prefer to be more isolated (Cavanaugh et al., 2004; Kerka, 1996).

Preference for Math, Science, and Technology

In regards to the mean differences between the two distinctive groups of the online students (i.e., indicating online discussion is helpful and otherwise), the ANOVA presented statistically significant differences on four subjects (i.e., science, literature, math, and technology). However, the analysis with SEM confirms only three subjects—math, science, and technology—as having significant correlations with online discussion.³ This implies that those who chose to study online (due to the potential reasons we discussed) are more likely to enjoy learning math, science, and technology in the online program and be more engaged in online discussions. However, it is not clear why the online interaction channel could be a more effective setting to learn math, science, and technology than literature or social science. It is also not clear if they favor online discussion because they are more math-, science-, and technology-inclined students. Furthermore, in the SEM analysis, the path with the highest coefficients presents that those online students who prefer working in groups (β = .23, p < .001) and enjoy learning math (β = .15, p < .001) are likely to practice task planning more (β = .16, p < .001) than the counter group. This is one of the paths analyzed to have the combination of highest coefficients. This certainly does not establish a substantive causal-effect relationship, but presents us with an intriguing phenomenon. In future study, an in-depth analysis of student group interactions online and their specific task-planning practices will help us better understand the path we identified.

To date, there is a dearth of research studies examining group-based online math learning scenarios. As one of very few studies, Stahl (2006) has examined how a small group of students could take advantage of online interaction tools to collaborate in learning math. In the study, each member of the student group took a turn in solving geometry problems in multiple incremental steps. Such online group cognition or a collective knowledge-building process seems to suggest new math education possibilities, but would certainly require in-depth research leading to new pedagogies and tools to successfully support students in online charter school settings.

Conclusion

As to date, this study is the first empirical study focusing on the online charter high school students in an attempt to determine the online students’ perceptions, preferences, and noncognitive traits. The findings present to us potential reasons for students to choose online learning, stark distinctions in their perceptions on the online discussion component, types of students who might potentially drop out or succeed in the online education program, subject preferences in online learning, and profound interrelationships among choice, subject, and self-regulatory dispositions.

The initial findings call for further studies to pinpoint detailed conditions (i.e., including perceptions, preferences, and traits) leading students to choose online programs (i.e., with particular options for different type, path, pace, medium, etc.) and possible ideal resources and support models for the students to be successful online learners. Further studies that also include academic performance data will enable us to not only examine these conditions, but also possibly determine their potential effects.

As 24% of parents in the United States move based on their school choice (Greene et al., 2010) and while virtual schooling is poised for explosive growth, an online charter school option will increasingly become an important new specie in the education ecosystem for at least two reasons: It may serve the student who could not or would not enroll in other options (e.g., personal or school quality reasons) and it may cause mediocre or bad schools to improve their education service quality in order to stay competitive in the ecosystem.

With continuous advancement in information technology, the future online learning environment will be drastically different from the present ones. At the same time, the fine line between brick-and-mortar schools and online schools will blur as more learning technologies become available to enhance pedagogies and student learning regardless of location, time, or pace. At the same time, students will have more school options and learning tools to combine best experiences of “all lines” of learning media for individualized prescriptive learning plans or collaborative learning (e.g., team-based, project-based, etc.) with student groups of various sizes and locations. Most of all, how to come up with educational programs that can help all students excel and reach their full potential is the key concern for all educators.

Footnotes

Notes

Paul Kim is a chief technology officer and assistant dean at Stanford University, 520 Galvez Mall #3084, Stanford, CA 94305; e-mail: phkim@stanford.edu. His areas of interests include technology-enabled pedagogic models, virtual team performance, learning technology ecosystems, and mobile technology for international development.

Flora Hisook Kim is a research assistant at Stanford University. She focuses on analyzing education system dynamics.

Arafeh Karimi is a research assistant at Stanford University. Her interests include student motivation in online learning and education enterprises in the private sector.

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