*UDGXDWH 6FKRRO &KRLFH $Q ([DPLQDWLRQ RI ,QGLYLGXDO DQG ,QVWLWXWLRQDO (IIHFWV
'DYLG (QJOLVK 3DXO ' 8PEDFK
The Review of Higher Education, Volume 39, Number 2, Winter 2016,
pp. 173-211 (Article)
3XEOLVKHG E\ -RKQV +RSNLQV 8QLYHUVLW\ 3UHVV
DOI: 10.1353/rhe.2016.0001
For additional information about this article
Access provided by Renmin University of China (7 Apr 2016 09:58 GMT)
https://www.doczj.com/doc/b11222591.html,/journals/rhe/summary/v039/39.2.english.html
174T he R eview of h igheR e ducaTion W inter 2016 more to the local, state, and federal governments in the form of higher tax payments. Graduate degree earners have also been shown to exhibit char-acteristics that lead to improved health, and they tend to have children who are better prepared for primary education and who subsequently attain higher levels of education over their lifetimes (Baum et al., 2010). Graduate education also provides the key engine for innovation and the discovery of knowledge. Many graduate students work side-by-side with faculty members in the pursuit of knowledge while pursuing their degrees and then carry this work forward both inside and outside the halls of academia (Kallio, 1995; Millett, 2003; Wendler et al., 2010).
While graduate degrees benefit individuals and society, there remains inequality regarding graduate school attainment for historically under-represented populations (Perna, 2004). First-generation, low-income, and minority student populations are less likely than their peers to earn a graduate degree (Morelon-Quainoo et al., 2009). Without significant and continued improvements in graduate attainment rates for underrepresented popula-tions, it is doubtful that the U.S. will maintain its status as a leader in the global economy (Wendler et al., 2010). Therefore, it is necessary to continue and expand empirical studies of the factors that influence an individual’s decision to pursue graduate education—particularly pertinent is the existing vein of research exploring college choice at the undergraduate level (Perna, 2004).
While graduate education has consistently grown in both size and impor-tance, theoretically-based scholarly research has lagged considerably behind that afforded to undergraduate college choice (Mullen, Goyette, & Soares, 2003). The majority of research on graduate choice has either been atheo-retical, lacking in strong theoretical underpinnings (Hearn, 1987), or has focused only on the factors influencing a population at a single institution (Poock & Love, 2001). While these studies make important contributions to the literature and the field in providing descriptive data on the graduate choice process, they do not help in the understanding of future graduate enrollment behaviors.
A number of researchers have recently acknowledged this fact, and con-ducted studies that drew from nationally representative samples (see, for example, Heller, 2001; Millett, 2003; Mullen et al., 2003; Perna, 2004; Zhang, 2005). These studies provided significant advances to the base of knowledge related to graduate school choice decisions. They are limited, however, in two regards. First, all five studies use the 1992–1993 Baccalaureate and Beyond (B&B:93) study and related follow-up surveys for their data. While there are benefits to this commonality (such as the ability to cross-reference the efficacy of variables and findings), a major drawback is the age of the data. Significant changes have occurred within American higher education and graduate studies in the time since the B&B:93 dataset was created. The costs associated with pursuing post-secondary and graduate education have in-
E nglish and U mbach / Graduate School Choice175 creased dramatically (Millett, 2003), and financing methods have changed significantly, with an increased focus placed on student loans (Hearn & Holdsworth, 2004). The second limitation is the continued lack of a theo-retical conceptual model for explaining the graduate choice process. Of the five studies referenced above, only Millett (2003) and Perna (2004) firmly ground their study within a theoretical framework derived from the literature. This lack of strong theory development hinders both applied and theoretical applications of the research.
This study seeks to fill in the gaps in previous research by applying a sound theoretical framework to a more recent nationally representative dataset.
The conceptual model used in this study is an adaptation of the one de-veloped by Perna (2006) in her analysis of undergraduate college choice. It draws from previous college choice research, integrating concepts of habitus, cultural capital, and social capital into the econometric framework of human capital theory. Graduate school choice, which is defined for the purposes of this paper as the decision to pursue any post-baccalaureate degree program at the master’s, doctoral-research, or doctoral-professional practice level, is shaped and determined by a number of individual and organizational level characteristics. The conceptual model posits that the graduate school choice process is comprised of three phases. First, an individual develops an aspiration for graduate education; second, the individual submits ap-plications to graduate schools; and third, the individual matriculates into a graduate program.
The purpose of this study is twofold: first, to continue the exploration of the impact of individual biographic factors on the decision to pursue gradu-ate school; and second, to assess how undergraduate institutional character-istics influence the graduate school choice process. The study investigates the efficacy of the proposed model by testing the impact that the theorized independent variables have on the dependent actions of aspiring to, applying for, and ultimately enrolling in graduate education. The dataset analyzed is the 2000/01 Baccalaureate & Beyond Longitudinal Study (B&B:00/01). The nationally representative B&B:00/01 study is comprised of approximately 10,000 students who received a baccalaureate degree from one of the over 1,000 institutions sampled between July 1, 1999 and June 30, 2001.
The following research questions guided the study of graduate school choice:
1. To what extent do measures of human capital explain graduate school
aspiration, application, and enrollment?
2. To what extent do student demographic and background characteristics
and the concepts of cultural capital and social capital influence graduate school aspiration, application, and enrollment?
3. To what extent do characteristics of the undergraduate institution influ-
ence graduate school aspiration, application, and enrollment?
176T he R eview of h igheR e ducaTion W inter 2016
B ackground and c onceptual F ramework
While considerable research has been devoted to the development and testing of college choice models at the undergraduate level, the concept of college choice for graduate school is less prevalent and somewhat less refined (Mullen et al., 2003). Research of graduate school enrollment processes has evolved through multiple iterations and approaches; early research, however, was largely descriptive, providing foundational knowledge about who actu-ally pursued graduate study in America (Kallio, 1995). Later works explored the concept of persistence into graduate study, primarily through the lens of a causal model derived from the work of Pascarella (1984), Tinto (1975), and others (Ethington & Smart, 1986).
Recent research has begun to situate graduate enrollment choice processes within theoretical models adapted from the established literature on under-graduate college choice (Perna, 2004). This more recent research explores a number of different variables associated with graduate education, including background characteristics inherent to the student, various aspects of the student’s undergraduate institution, the student’s undergraduate major, and the total amount of debt the student accumulated while pursuing the baccalaureate degree. Drawing from each of these research threads, this study continues the exploration of the graduate school attendance decision within a model derived from the research on undergraduate college choice. Building from established college choice conceptual models allows current research to frame the student’s process of electing to pursue graduate study as a distinct and new choice among many possible post-graduation options. Theoretical Framework
This study draws from existing literature on college choice and graduate school enrollment and is grounded primarily in the economic framework of human capital theory. Human capital theory has been the most widely used approach for exploring choice decisions related to undergraduate and graduate education (Paulsen & Toutkoushian, 2008). While human capital theory provides the primary lens for examining graduate school choice deci-sions in this paper, we explore additional perspectives through the overlay of the concepts of cultural capital and social capital. Recent work by Perna (2004) proposed and validated that the inclusion of cultural capital and social capital concepts in choice models improves their overall efficacy.
Human capital is an economic theory that posits that an individual’s ability to produce economic value is directly related to the knowledge, skills, and abilities he or she possesses (Becker, 1962). The theory grew out of economists’ conclusion that investments in human capital could have as equally profound an impact on economic productivity as the more traditional concepts of material and physical capital (Cohn & Geske, 1990). Educational
E nglish and U mbach / Graduate School Choice177 researchers often apply human capital theory as a mechanism for explaining the benefits of obtaining further education, as well as the decision-making process individuals undergo when considering such additional education (Levin, 1989). Within education economics, human capital theory finds broad usage and adoption in studies that explore issues of choice and deci-sion sets. The theory generally suggests that investment in education results in increased employee competencies, allowing them to contribute at a higher level, and subsequently, demand a higher wage (Thomas & Perna, 2004). Thus, within the context of graduate school choice, a potential student weighs the perceived set of knowledge and skills that could be obtained in pursuing an advanced degree against the current price of attendance. The decision is shaped by individual preferences and constrained by the individual’s bud-getary limitations (Paulsen & Toutkoushian, 2008).
The initial research into college choice commonly employed either an econometric or a sociological framework, largely owing to the disciplinary backgrounds of the individual researcher (Hossler, Braxton, & Coopersmith, 1989). Models began to combine the two approaches rather quickly, as schol-ars posited that overlaying sociological concepts of human behavior on exist-ing econometric frameworks would better explain the college choice process (Perna, 2006). Research on choice processes at both the undergraduate and graduate level has moved in this direction in recent years, incorporating the theories of cultural capital and social capital.
Cultural capital is a sociological theory that has received extensive usage in the study of educational outcomes (Reay, 2004). Cultural capital refers to the broad set of cultural knowledge that an individual acquires from a parent, grandparent, or caregiver (Perna, 2006). The extent of the level of acquisition is typically a function of the individual’s relationship to the dominant cultural paradigm, manifested in the form of language, activities, artistic experiences, and preferences (Dumais, 2002). The amount of cultural capital possessed by an individual therefore influences his or her ability to navigate an educational choice process that inherently values the cultural norms perpetuated by the established structure, of which the educational institution is a component (Perna, 2006).
A related but distinct concept is social capital theory. Social capital is the network of connections and resources that an individual has access to (Veenstra, 2009). The individual is then able to capitalize or draw upon those connections and resources in support of an objective, such as an educational choice decision (McDonough, 1997; Sandefur, Meier, & Campbell, 2006). In the context of educational choice, increased levels of social capital can be tapped to provide insights into the admissions applications process and obtain assistance in the pursuit of financial aid (O’Connor, Hammack, & Scott, 2010; Perna, 2006).
178T he R eview of h igheR e ducaTion W inter 2016 The theoretical model employed in this study integrates the concepts of cultural and social capital into the established econometric framework of hu-man capital. This integration allows for the use of a rational utility approach while acknowledging and accounting for differences that sociological factors such as gender, race/ethnicity, socioeconomic status, geographic location, etc. have on the graduate school choice process. Moreover, the inclusion of cultural capital and social capital theories helps address a number of al-ternatives to human capital theory that have been advanced to explain the relationship of educational attainment to socioeconomic status, including the theories of screening, signaling, sorting, and credentialism.
Broadly, these alternative theories question the efficacy of human capital in explaining ties between education, productivity, and employment. Theories such as screening, sorting, and credentialism argue that receipt of additional education acts as a proxy for inherent talents or abilities and that employers use that education as a short-cut in the evaluation of potential employees (Bills, 2003). The competing theories are similar to human capital, however, in the fact that they posit that the increased educational attainment manifests itself in the form of higher wages (Kjelland, 2008). As such, researchers have historically found it difficult to empirically test the efficacy of the compara-tive models (Lang & Kropp, 1986).
A number of sociological researchers have found that the theories of screening, sorting, credentialism, etc. provide an attractive alternative to human capital theory, particularly when examining college choice decisions of non-majority and historically underrepresented populations (Bills, 2003). This is because human capital theory, when applied to educational choice decisions, holds an assumption of student rationality that is not explicitly concerned with factors that shape educational preferences, including race, ethnicity, gender, first-generation college status, etc. These factors, however, hold primary interest for sociology researchers, who have concerns with the equitable allocation of educational and employment outcomes (Perna, 2006).
While the inclusion of social capital and cultural capital theories in the framework does not address every concern brought forth by advocates of the theories of screening, signaling, and credentialism, it does ameliorate a number of the issues by taking into account students’ backgrounds, origins, and available resources. Studies at both the undergraduate and graduate level of the choice process have found that the inclusion of cultural and social capital constructs in the traditional econometric framework improves the explanatory power of the model (Perna, 2000; Perna, 2004).
The theoretical model advanced in this paper posits that cultural capital and social capital are used by an individual consistent with his or her habitus. The concept of habitus has received substantial attention within the study of educational choice processes, and is most often drawn from the work of
E nglish and U mbach / Graduate School Choice179 Bourdieu (Reay, 2004). In this application, the habitus is conceptualized as the internal set of perceptions and beliefs about the world an individual has, inclusive of their place in that world (Dumais, 2002). Educational researchers have advanced the use of habitus in educational choice decisions as a way of contextualizing the extent to which a student’s lived experiences “fit” with the expectations of the college or university (Nora, 2004).
The model is derived from the work of Perna (2006), who advanced a model of college choice that incorporated the concepts of habitus, cultural capital, and social capital into a traditional rational utility human capital structure. Perna (2006) drew from McDonough’s (1997) work that put forth an organizational habitus comprised of various resources and social struc-tures that influence choice processes. This concept of habitus is comprised of the student’s gender, race, ethnicity, and socioeconomic status. The habitus also operationalizes the concepts of cultural and social capital as related to choice processes manifested through a human capital investment decision.
Perna (2006) posited that the habitus, inclusive of various measures of cultural and social capital, shapes the ways in which an individual approaches and considers an enrollment choice decision. The habitus, conceptualized as an internal schema of thoughts, beliefs, and perceptions, is directly formed and influenced by the environment in which an individual exists and is unique to that individual (Perna, 2006). As such, each individual experi-ences and navigates the choice process differently, from within their own situated context.
Conceptual Model
The conceptual model advanced in this paper is adapted from the one developed by Perna (2006) for use in analyzing undergraduate college choice decisions. Perna’s model situated the decision of whether to enroll in college as a decision nested inside four discrete contextual layers: social, economic, and policy context (layer 4); higher education context (layer 3); school and community context (layer 2); and habitus (layer 1). An individual’s analysis of expected benefits and costs is shaped by the four layers when determining whether to pursue a baccalaureate degree, and are simultaneously influenced and constrained by demand factors for higher education and availability of resources.
At its core, Perna’s (2006) model focuses on the human capital, cultural capital, and social capital theories. We theorize that these three components of the model, as included in the habitus layer, are still in effect in the graduate school context, although the specific variables used to measure those com-ponents are necessarily different. For instance, a student’s undergraduate major is a measure of human capital in the graduate school model, and this variable obviously was not included in Perna’s original model that focused on the transition from secondary to postsecondary education. The model
180T he R eview of h igheR e ducaTion W inter 2016 is therefore adapted in this study to allow for the examination of the gradu-ate school choice process. We update various components of the habitus to reflect the transition from undergraduate education to graduate education, as opposed to the secondary to post-secondary transition originally posited by Perna (2006). The second layer is modified from a school and commu-nity context to now encapsulate the effects of the undergraduate institution the student attended. The third layer now focuses on the graduate school context, instead of higher education, and the fourth layer remains the social, economic, and policy context.
The conceptual model posits that a student’s situated position within the four contextual layers shapes their decision to pursue graduate education. That decision manifests itself in the form of the graduate school choice process, which is comprised of three nested phases. First, a student develops an aspiration to obtain a graduate-level degree. Next, the student takes the active step of submitting applications to graduate schools. Enrollment in a graduate degree program constitutes the final phase of the process.
This conceptual model extends Perna’s (2006) previous work by modifying an approach designed to model undergraduate college choice and applying it in the study of graduate school choice. We believe the model, previously shown to facilitate higher levels of predictive power, fits well within the graduate school frame. The outlined changes to the model specifically address and account for the differences that exist between graduate school choice and undergraduate college choice decisions.
The use of this model, appropriately modified to fit the graduate school choice process, facilitates a conceptual structuring and operationalizing of the theories of habitus, human capital, cultural capital, and social capital (Perna, 2006). We limited this study to an examination of only the first two layers of the conceptual model (Habitus and Undergraduate Institution Con-text), as we wanted to isolate the individual and institutional level variables in analyzing their effects via the multilevel models. As the model suggests, this study examines the intersection of an individual’s background charac-teristics with the traits inherent to the institution from which the individual received their baccalaureate degree. By doing so, it is one of the first studies to explicitly explore the varying influences that an individual’s biographic and demographic attributes and undergraduate institutional characteristics have on the decision to pursue a graduate degree. The operative two-level model is presented as Figure 1.
Variable Selection
The decision of which variables to include for analysis in the conceptual model was informed by a thorough review of previous research into graduate school choice. As a number of common independent variables exist in the current literature, we examined those most salient to the theoretical model
E nglish and U mbach / Graduate School Choice181
adapted from Perna’s (2006) analysis of undergraduate college choice pro-cesses. Specifically, we look at the first two layers of the conceptual model, the habitus and the undergraduate institution context, and their impact on the human capital investment decision, which is the core of the model.
The center of the conceptual model proposed by Perna (2006) and adapted for use in exploring graduate school choice processes is the human capital investment decision. Higher undergraduate grade point averages consis-tently and positively affect graduate enrollment decisions (Fox, 1992; Heller, 2001; Millett, 2003; Zhang, 2005), and undergraduate major has regularly influenced the demand for graduate education (Heller, 2001; Mullen et al., 2003; Zhang, 2005). Of particular interest for this study was the difference in how Perna (2004) and Millett (2003) operationalized undergraduate major in their respective analyses of graduate school choice. Millett (2003) catego-rized undergraduate majors according to the Biglan system, which defines
182T he R eview of h igheR e ducaTion W inter 2016 fields as either pure or applied. Her study posited and found that students who completed a pure undergraduate major were twice as likely to apply to graduate school than those who pursued an applied field. Perna (2004), however, employed a strict human capital classification, and considered the undergraduate major as a proxy for expected foregone earnings when enrolling in graduate school, finding a connection between lower expected earnings and higher rates of graduate school enrollment.
The total amount of debt the student has accumulated at the undergradu-ate level and the potential wages the student would forego by enrolling in graduate school moderate that demand. The impact of undergraduate debt has been explored extensively in the literature (Ekstrom, Goertz, Pollack, & Rock, 1991; Fox, 1992; Heller, 2001; Millett, 2003; Perna, 2004; Schapiro, O’Malley, & Litten, 1991; Weiler, 1991), with little to no consensus found concerning whether debt has a positive impact, a negative impact, or no impact on the graduate school choice process (Millett, 2003).
The first contextual layer of the adapted Perna (2006) model, the habi-tus, contains the individual-level student demographic variables of race, ethnicity, and gender. Previous research into graduate school choice has shown males to be more likely than females (Hearn, 1987; Weiler, 1991) and underrepresented minority students to be less likely than their peers (Johnson, 1996; Millett, 2003; Poock & Love, 2001; Zhang, 2005) to pursue graduate education. These are not consensus findings, however, as Perna (2004) found females to be more likely than males to enroll in masters level programs, but less likely to enroll in doctoral level programs. Perna (2004) and Millett (2003) have also shown that African-American and Hispanic/ Latino students are more likely to pursue graduate education when account-ing for all other variables in their respective models.
The conceptual model adapted from Perna (2006) next incorporates ap-proximations of cultural capital and social capital in the analysis of graduate school choice. The analysis of secondary datasets such as the Baccalaureate and Beyond study are very limited in their direct measures of cultural and social capital; indirect approximations are therefore used to attempt to account for these considerations in the conceptual model (Perna, 2004). Variables previously analyzed in the literature include the parents’ highest level of education (Hearn, 1987; Fox, 1992; Millett, 2003; Pascarella, 1984; Perna 2004; Weiler, 1991), parents’ income (Millett, 2003; Zhang, 2005), parents’ contributions towards tuition costs (Perna, 2004), and whether English was the primary language spoken in the home (Perna, 2004). Finally, the variables of high school type and whether the student attended college at an out-of-state institution (Perna, 2004) are included as proxies for social capital. Perna (2004) theorized that students who attended an undergraduate institution outside of their state of residence would have access to broader
E nglish and U mbach / Graduate School Choice183 social networks, and thus greater exposure to graduate opportunities. The same rationale applies to the concept that attending a private high school would indicate greater access to broad social networks.
The second contextual layer of the model adapted from Perna (2006) includes variables related to the undergraduate institution. Characteristics of the undergraduate institution attended, such as institutional control and Carnegie classification, have frequently appeared in the literature (DeAngelo, 2009; Mullet et al., 2003; Perna, 2004; Zhang, 2005). We also explore whether attending a Historically Black College or University (HBCU) influences the three phases of the graduate school choice process. These institutional characteristic variables also provide key insights on the influence of social capital in the graduate school choice process (Perna, 2004).
While institutional selectivity has frequently been included as a proxy for institutional quality in previous studies (Fox, 1992; Millett, 2003; Mullet et al., 2003; Zhang, 2005) we excluded it from our analysis after reviewing a number of articles that question the rationale of accepting institutional selec-tivity as a proxy for the academic quality of the institution. In their analysis of selectivity and educational quality, Kuh and Pascarella (2004) noted that measures of institutional selectivity are typically determined largely by av-erage SAT/ACT scores. Their analysis found institutional selectivity to be weakly associated with the quality of the student’s undergraduate education.
A similar analysis was conducted by Pascarella et al. (2006), finding that institutional admissions selectivity served as a poor signal for the quality of a student’s undergraduate education, and was perhaps indicative of why the majority of previous research failed to find a significant association between the two measures. While we did not include institutional selectivity, we did explore whether the cohort graduation rate of the student’s undergraduate institution affected the graduate school choice process.
Dependent Variables
For the purposes of this study, graduate school choice refers to the three-phase process of aspiration, application, and enrollment by which an individual makes the determination to pursue a post-baccalaureate degree, at either the master’s or doctoral (research or professional practice) level. As the focus of this study is on exploring the impacts of individual and in-stitutional characteristics on graduate education in general, the dependent variable of interest is not divided into various graduate degree disciplines and types (e.g., non-terminal masters, terminal masters, doctoral-research, doctoral-professional practice). While there are certainly a number of differ-ences that exist between those graduate degrees, a full examination of their manifestations is beyond the scope of this study.
We conceptualize the graduate school choice process as being comprised of three nested phases. First, a student develops an aspiration for pursuing a
184T he R eview of h igheR e ducaTion W inter 2016 graduate degree. Next, the student submits applications to graduate school. Lastly, the student enrolls in a graduate program. As such, the dependent variables of interest were derived from the B&B:2000/01 variable Graduate School Pipeline (GRDPIP). This variable contained information about a student’s progression through a hypothetical pipeline of graduate education (no plans to attend graduate school, plans graduate school in the future, applied for graduate school, accepted to graduate school but not enrolled, and enrolled in graduate school).
We derived three dichotomous dummy variables from this initial vari-able: graduate school aspiration (GRAD_ASP); graduate school application (GRAD_APPLY); and graduate school enrollment (GRAD_ENROLL). These dummy variables were created by “rolling down” all higher-level responses. Graduate school enrollment was coded “1” only for those students enrolled in a graduate program; graduate school application was coded “1” if the student responded that they had either applied for, been accepted to, or enrolled in graduate school; and graduate school aspiration was coded “1” for all stu-dents who responded that they planned to attend graduate school, as well as those that had applied for, been accepted to, or enrolled in graduate school.
In summary, the individual and societal benefits of obtaining a gradu-ate degree are significant, and it is believed that this study advances the understanding of not only the variables that impact the decision to pursue graduate study, but also the actual choice processes as they exist in established theoretical frameworks.
a nalytical approach
This study uses data from the 2000/01 Baccalaureate and Beyond Longi-tudinal Study (B&B:2000/01). The B&B: 2000/01 study was conducted on behalf of the National Center for Education Statistics (NCES), an agency within the U.S. Department of Education’s Institute of Education Sciences (Charleston, Riccobono, Mosquin, & Link, 2003). The Baccalaureate and Beyond research report serves as the NCES’s primary tool for studying the lives and post-graduation activities of baccalaureate recipients, inclusive of graduate education, work experiences, financial situations, and personal experiences (Charleston et al., 2003)
Missing Data
The analysis showed that the 10,030 student records were distributed across 690 undergraduate institutions. Our first objective was to address the issue of missing data within the dataset; we used multiple imputation procedures to handle item-missing data. A fully conditional specification model that applied the appropriate sample weights as directed by NCES was employed to generate a set of ten imputed datasets. Van Buuren (2007)
E nglish and U mbach / Graduate School Choice185 conducted simulations testing the efficacy of this fully conditional approach and found that when the traditional joint modeling approach was infeasible (due to the inclusion of non-normal independent variables), the fully con-ditional approach yielded less bias. We accounted for the clustered sample design by including the undergraduate institution ID as a predictor value in the multiple imputation procedure, as recommended Reiter, Raghuna-than, and Kinney (2006). Although an approach that explicitly modeled the multilevel structure would have been preferred, the methodology employed in this paper is more sophisticated than the alternative approaches taken in previous graduate school choice studies (e.g., Millet, 2003; Mullen et al., 2003; Perna, 2004: Zhang, 2005).
While early research into multiple imputation recommended the genera-tion of five to ten datasets, more resent analyses have suggested that 20 or more implicates is appropriate (Schafer & Graham, 2002). We ultimately selected to generate ten imputed datasets for the multilevel analysis, as that is the maximum number that HLM 7 is able to process. This limitation informed our decision to include the dependent variable in the imputation process but then delete all cases in which its value had been originally miss-ing (Allison, 2002). This approach is known as multiple imputation, then deletion (MID), and has been shown to offer more efficient estimates (von Hippel, 2007). Both von Hippel (2007) and Young and Johnson (2010) recommended this approach when the number of imputed datasets is small. This resulted in a reduction of the sample size from 10,030 to 9,770. Modeling Strategy
The methodology employed in this paper is generalized hierarchical linear modeling with a binomial distribution and a logit link, in which students are nested within an undergraduate institution. The generalized hierarchical linear model is a type of multilevel model that is useful when individuals are situated within an institution and the dependent variable of interest is not continuous (Heck, Thomas, & Tabata, 2010). The generalized hierarchical linear model employed in this paper is appropriate for two reasons, one technical and the other theoretical. First, the sampling methodology em-ployed in the construction of the B&B:2000/01 was a multi-stage design, in which institutions were first selected and students were subsequently drawn from that set. As such, one of the common and required assumptions of ordinary least squares (OLS) regression, independence of observations (also referred to as uncorrelated disturbances), has been violated (Allison, 1999). If a standard OLS regression model were used and the clustered nature of the data was not accounted for, it is possible that the analysis would yield standard errors that are too small, increasing the chance of receiving a Type I error. (Garson, 2011).
186T he R eview of h igheR e ducaTion W inter 2016 The use of a multi-level modeling approach is also of theoretical benefit. As OLS regression only allows for the modeling of variance at a single level, it would be impossible to jointly model the impact of both individual and institutional level effects on the graduate school choice process simultane-ously. Multi-level modeling, however, can concurrently explore the impact of individual and institutional effects because the beta coefficients are not treated as fixed effects, as in OLS, but as random effects drawn from a normal distribution of betas (Garson, 2011). We grand-mean centered all variables included in the analysis. This transforms the interpretation from being based on the raw score of interest to being based upon an observed values’ devia-tion from the mean of all values in the dataset (Luke, 2004). All models were weighted at both levels of analysis. The NPSAS institutional variable INSTWT was selected as the level-two weight, while the derived weight STU_WT was specified at level one. The STU_WT variable was calculated by dividing the analysis weight (BB01AWT) by the institution weight (INSTWT) (Pfeffer-mann, Skinner, Homes, Goldstein, & Rasbash, 1998).
Cluster Size Considerations
The number of students sampled from any specific undergraduate institu-tion varied from a low of one student to a high of 91. Multilevel modeling requires a sufficient number of level-two clusters, as well as a satisfactory number of level-one units contained within each cluster (Heck et al., 2010). The decision of which cut point to use for removal of institutions with small sample sizes depends primarily on the analytical objectives of the study (O’Connell, Goldstein, Rogers, & Peng, 2008). As such, we elected to remove any institution in the sample containing less than five students from the da-taset, as our primary objective was to examine the regression coefficients in the multilevel models (Hox, 2010). The work of Meinck & Vandeplas (2012) further informed this decision. They conducted an empirical simulation evaluating sample size requirements in hierarchical linear models, finding that smaller sample sizes were of less concern when estimating fixed model parameters, and that large level-two sample sizes helped limit the overall residual variance generated by small level-one sample sizes.
The decision to delete only institutions with fewer than five students was also motivated by the large amounts of additional data that would have been lost by increasing the threshold. For example, deleting all institutions with fewer than ten students would have resulted in a loss of 270 institutions and 1,240 students; a 20 student threshold would have increased these figures to 520 institutions and 4,800 students. Our final decision to delete institutions with fewer than five students resulted in a reduction of 125 undergraduate institutions and 260 student records. The final dataset prepared for analy-sis, therefore, contained 9,480 students nested within 560 undergraduate institutions.
E nglish and U mbach / Graduate School Choice187
Model Estimation
We conducted three separate sets of generalized hierarchical linear models to explore the influence of individual and institutional factors on the three phases of the graduate school choice process of aspiration, application, and enrollment. As the dependent variable of interest in each of these models was dichotomous (aspire/not aspire, apply/not apply, enroll/not enroll) the dependent measure was transformed into a form that would allow a linear modeling of the independent variables of interest. All output contained in this paper was estimated via an adaptive Gaussian quadrature approach and is provided in the unit-specific format with robust standard errors. The adaptive-Gaussian quadrature approach uses numerical integration to approximate full maximum likelihood methods in generalized hierarchical linear models (Pinheiro & Bates, 1995). The adaptive-Gaussian quadrature method of is computationally intensive but has been shown to produce consistent and asymptotically unbiased estimates (Raudenbush, Yang, & Yosef, 2000). Yosef (2001) and Johnson (2006) also found the adaptive-Gaussian quadrature approach to perform very well when cluster sizes were small in Bernoulli models and Poisson models, respectively. This strong performance with small cluster sizes informed our decision to select the adaptive-Gaussian quadrature approach, as our cut-point of no fewer than five-students per undergraduate institution still allowed for relatively small level-two cluster sizes.
Model Building
We tested for multicollinearity before beginning the process of construct-ing the multilevel models. We found that all independent variables included in the analysis exhibited variance inflation factors smaller than two, which indicated that multicollinearity was not a cause for concern (Mertler & Van-natta, 2010). We next calculated the intraclass correlation (ICC) using the formula outlined by Snijders and Bosker (1999) for use in situations where the dependent variable of interest is dichotomous. We calculated an ICC (proportion of variance explained by undergraduate institution) of 12.62% for the aspiration model, 7.15% for the application model, and 6.69% for the enrollment model. While this may seem like a trivial amount of variation to attempt to model via a multilevel approach, Porter and Swing (2006) argue that there are a number of reasons to take this approach. First, quantitative survey research rarely explains more than 30% of the variance observed; as such, the variance witnessed here is not an insignificant amount. They also note that small ICC calculations have not limited prior educational research and that it is still possible to find independent variables that influence the dependent variables of interest.
We next created the full within-institution model. This model, also known as the level-one individual model, or the random intercept model, included
188T he R eview of h igheR e ducaTion W inter 2016 all level-one variables in the analysis, along with a randomly varying (by institution) intercept. At level-one we included a series of variables that represented various demographic characteristics and aspects of cultural and social capital (see Table 1 for descriptive statistics of all variables included in the models). The next step in the model-building process was to construct the full model, which allows the intercept to vary as a function of a set of level-two predictor variables. The level-two variables included in each of the models (aspiration, application, and enrollment) include whether the student’s undergraduate institution was an HBCU, the institution’s cohort graduation rate, the institution’s control (public/private/for-profit), and the institution’s Carnegie Classification.
r esults
Descriptive Analysis
Table 1 presents descriptive statistics for individual-level variables in-cluded in the models. Of the 9,480 students included in the final analysis, 83% indicated that they aspired to graduate education. Approximately one third had applied for graduate school, and just over one quarter were actu-ally enrolled in a graduate program. There were substantially more females than males in the weighted sample used for analysis, with 58% female and 42% male. White, non-Hispanic students represented nearly three quarters of the sample. The largest group of students (34%) majored in the humani-ties or social/behavioral sciences; the second largest category belonged to business/management majors.
Examination of the level-two (undergraduate institution) descriptive statistics revealed a number of interesting facets of the Baccalaureate and Beyond 2000/01 Longitudinal Study data (see Table 2). In terms of institution type, the largest group of students included in the study graduated from an institution classified as a doctoral/research university (47%). Graduates of master’s institutions comprised the second largest cohort at approximately 36%; students from baccalaureate colleges (liberal arts and general) repre-sented only 12% of the total population. The majority of students (66%) graduated from a public institution, while one third completed their bacca-laureate degree at a private not-for-profit college or university. The average cohort graduation rate for all institutions included in the study was 53%. Multilevel Models
Table 3 provides the coefficients, odds ratios, and levels of significance for each of the multilevel models (aspiration, application, and enrollment). The coefficients presented represent the change in the log-odds of the dependent variable of interest being equal to one when there is a one-unit change in the
E nglish and U mbach / Graduate School Choice189
t aBle 1.
l evel-one v ariaBles: d escriptive statistics
Variable Name N Mean Std. Error
Dependent Variables
Graduate Aspiration 9,480 0.831 0.000 Graduate Application 9,480 0.331 0.000 Graduate Enrollment 9,480 0.267 0.000 Demographic Variables
Female 9,480 0.576 0.000 White 9,480 0.736 0.000 Black 9,480 0.080 0.000 Hispanic or Latino 9,480 0.086 0.000 Asian 9,480 0.059 0.000 AI/AN/NH/PI 9,480 0.011 0.000 Other or two or more races 9,480 0.028 0.000
Human Capital Variables
Undergraduate GPA 9,480 3.163 0.101 Business 9,480 0.201 0.001 Humanities & Social/Behavioral Sciences 9,480 0.341 0.001 Math/Life & Physical Sciences 9,480 0.090 0.001 Comp/Info Sciences/Engineering 9,480 0.088 0.001 Education 9,480 0.091 0.001 Health 9,480 0.079 0.001 Vocational/Technical/Other 9,480 0.101 0.001 Cumulative Borrowed for Undergrad 9,480 13,535.890 14.382 PLUS Loans – Cumulative Through 2000 9,480 1,597.020 6.319 Cultural and Social Capital Variables
English Primary Language 9,480 0.113 0.002 Attended college out of state of residence 9,480 0.218 0.000 Parent’s education: HS Diploma or Less 9,480 0.287 0.003 Parent’s education: Some College/Training 9,480 0.201 0.003 Parent’s education: Bachelor’s Degree 9,480 0.242 0.003 Parent’s education: Master’s Degree 9,480 0.163 0.002 Parent’s education: Doctor’s Degree 9,480 0.107 0.002 Dependent: Less than $29,999 9,480 0.097 0.000 Dependent: $30,000 - $59,999 9,480 0.163 0.000 Dependent: $60,000 - $99,999 9,480 0.187 0.000 Dependent: More than $100,000 9,480 0.126 0.000 Independent: Less than $19,999 9,480 0.208 0.000 Independent: $20,000 - $49,999 9,480 0.137 0.000 Independent: More than $50,000 9,480 0.081 0.000 Cultural and Social Capital Variables
HS: Public 9,480 0.843 0.001 HS: Private 9,480 0.150 0.001 HS: Foreign 9,480 0.008 0.000 First year parent/relative pay tuition: None 9,480 0.469 0.009 First year parent/relative pay tuition: Some 9,480 0.136 0.002 First year parent/relative pay tuition: All 9,480 0.203 0.003 First year parent/relative pay tuition: N/A 9,480 0.192 0.011
190T he R eview of h igheR e ducaTion W inter 2016
t aBle 2.
l evel-two v ariaBles: d escriptive statistics
Variable Name N Mean Std. Error Undergraduate Institution Variables
BA Institution HBCU 9,480 0.019 0.000 Graduation Rate 9,480 52.918 0.053
BA Control: Public 9,480 0.657 0.000
BA Control: Private 9,480 0.330 0.000
BA Control: Private for Profit 9,480 0.013 0.000 Carnegie: Doctoral 9,480 0.474 0.000 Carnegie: Masters 9,480 0.361 0.001 Carnegie: Baccalaureate 9,480 0.119 0.001 Carnegie: Other 9,480 0.046 0.001
independent variable (Perna, 2004). The table displays the final “full” model containing all level-one (Habitus) and level-two (Undergraduate Institution Context) variables.
In this study, a number of variables representative of the human capital investment decision served as the building blocks for the generalized hi-erarchical linear models. We found that a student’s undergraduate major significantly and consistently influenced all three phases of the graduate school choice process. Compared to the reference category of having majored in business/management, students who studied in the humanities, social sciences, behavioral sciences, mathematics, and life or physical sciences were consistently more likely to aspire to, apply for, and enroll in graduate school. Education majors were more likely to aspire to graduate school, but did not differ significantly from business students when comparing their rates of applying to or enrolling in graduate school. Students who majored in computer or information sciences, engineering, health related fields, or any other major did not significantly differ from business students at any stage of the graduate school pipeline.
A student’s undergraduate academic performance, as measured by their cumulative grade point average, is significantly related to graduate school aspiration, application, and enrollment. We found the association was at its weakest point in graduate school aspiration, and at its strongest in the gradu-ate school enrollment model. The amount of debt a student accumulated while completing the baccalaureate degree was theorized to have a negative influence on graduate school aspiration, application, and enrollment. How-ever, we did not find student debt nor Parent/PLUS loans to be statistically significant (p < .05) variables at any stage of the graduate school process.
E nglish and U mbach / Graduate School Choice 191
t a B l e 3.F u l l g e n e r a l i z e d h i e r a r c h i c a l l i n e a r m o d e l s
A s p i r a t i o n M o
d e l
A p p l i
c a t i o n M o
d e l
E n r o l l m e n t M o d e l
C o
e
f fi c
i
e n
t
O
d
d s
R
a
t
i
o
C o
e
f
fi c i
e
n
t
O d
d
s
R
a
t
i
o
C o e f fi c i e n t O d d s R a t i o
I n t e r c e p t , β
0 1
.822**
* 6.1
89 -
0.802
*** 0
.448 -
1.
1
3
8*** 0.320
H u m a n C a p i t a l V a r i a b l e s T o t a l D e b t 0.000+ 1.000 -0.000 0.999 -0.000 0.999 P L U S L o a n s -0.000 0.999 -0.000 0.999 0.000 1.000 M a j o r : H u m a n i t i e s & S o c i a l /B e h a v . S c i . 0.605*** 1.832 0.619*** 1.857 0.432**
1.540 M a j o r : M a t h & L i f e /P h y s i c a l S c i e n c e s 0.781**
2.183 1.019*** 2.772 0.869*** 2.384 M a j o r : C o m p ./I n f o . S c i . & E n g i n e e r i n g 0.060 1.062 0.186 1.205 0.142 1.153 M a j o r : E d u c a t i o n 0.987*** 2.683 -0.014 0.986 -0.143 0.867 M a j o r : H e a l t h 0.230 1.260 0.166 1.181 0.147 1.158 M a j o r : V o c a t i o n a l /T e c h ./P r o f ./O t h e r -0.047 0.954 -0.110 0.895 -0.231 0.794 M a j o r : B u s i n e s s /M g m t . (R e f e r e n c e ) U n d e r g r a d u a t e G P A 0.004*** 1.004 0.005*** 1.005 0.007*** 1.007
D e m o g r a p h i c V a r i a b l e s F e m a l e -0.178 0.837 0.031 1.031 0.018 1.018 M a l e (R e f e r e n c e ) R a c e : B l a c k , n o n -H i s p a n i c 1.300*** 3.671 0.751*** 2.118 0.426* 1.531 R a c e : H i s p a n i c o r L a t i n o 0.676* 1.965 0.482* 1.619 0.214 1.239 R a c e : A s i a n -0.194 0.823 0.054 1.056 -0.066 0.935 R a c e : A m . I n ./A K o r H I N a t i v e /P a c . I s . 0.623 1.865 -0.069 0.933 -0.217 0.805 R a c e : O t h e r 0.142 1.153 0.219 1.245 0.175 1.191 R a c e : W h i t e , n o n -H i s p a n i c (R e f e r e n c e )
雅思口语范文：A perfect trip / holiday 2014-02-18 17:00 类别：雅思口语来源：enguo 责编：meten Describe a perfect trip or holiday. You should say: Where you would go. When you would go. Whom you would go with. What you are going to do. Why you think it was perfect. 1. a) If I had to describe a perfect trip, I would like to tell you that I would love to undertake a trip to the zoo. b) I am not sure if you have been there before. 2. a) I would like to go there in the summer. b) That is to say, I want to go there when the weather is fine, and all the animals are active. 3. a) I would take some of my friends with, and also my Biology teacher. b) What I mean to say is that I would like some nice company, but would also want somebody who would be able to explain many of the things that we are going to see there to me. There are several reasons as for why I would like to go to the zoo. Allow me to explain by mentioning some of them briefly. 4. a) First of all, I love nature and the outdoors. b) What I mean to say is that we would be out in nature the whole day. 5. a) Secondly, I would be able to see many interesting animals. b) For instance, I have heard that there are tigers, bears, wolfs, and many other animals. 6. a) Lastly, it would be a perfect trip for me because of the scenes that I would be able to see.
初中英语阅读教学中学生思维能力培养的探索与实践
初中英语阅读教学中学生思维能力培养的探索与实践 【摘要】在英语阅读教学中，教师应当灵活的运用教材，采用新颖的教学方法，设计出具有创造性和思维性的语言训练项目，发展学生的思维能力。本文以阅读教学为例，分别阐述了培养学生的分析、概括、发散和评判思维能力的方法与途径，从而使学生在学习过程中发展综合运用语言的能力，培养创新精神，增强实践能力。 【关键词】思维能力；阅读教学；初中英语 【基金项目】本文系江苏省苏州市教育科学“十二五”规划重点立项课题“基于‘ARCS学习动机模型’的初中英语阅读教学策略的研究”（课题批准文号：130703415）的部分研究成果。 中图分类号：G633.41 文献标识码：A 文章编号：1671-0568（2016）13-0063-03 《义务教育英语课程标准》（2011年版）明确指出：“义务教育阶段的英语课程具有工具性和人文性双重性质。就工具性而言，英语课程承担着培养学生基本语言素养和发展学生思维能力的任务，即学生通过英语课程掌握基本的语言知识，发展基本的英语听、说、读、写技能，初步形成用英语与他人交流的能力，进一步促进思维的发展，为今后继续学
习英语和用英语学习其他相关科学文化知识奠定基础。”因此，在实际英语教学中，培养学生的思维能力有着重要的现实意义。 思维是人们在认识活动中运用概念、判断、推理等思维形式，是人的大脑对客观事物的认识过程，是在实践的基础上产生和发展的，也是通过概念、判断和推理等形式来反映客观事物的能动过程。思维能力包括：理解力、分析力、综合力、比较力、概括力、抽象力、推理力、论证力、判断力等能力，它是整个智慧的核心，参与、支配着一切智力活动。语言是思维的载体，在学习语言的过程中训练思维是切实有效的方法之一。那么如何培养学生的这些思维呢？下面笔者就以牛津译林版的教学实践为例，探讨在初中英语阅读教学中提高学生思维能力的可行性。 一、创设合理“暗示”，激活学生的分析思维能力 心理暗示是指用含蓄间接的方式对别人的心理和行为 产生影响。“暗示”的作用是巨大的，不但可以影响到人的心理与行为，还能影响到人的思维分析能力。所以教学过程要通过暗示建立无意识的心理倾向，创造强烈的学习动机，开发潜力，提高学生的分析思维能力，以充分发展学生的“自我”。在课文的导入阶段，教师可以适时地、合理地借助“暗示”，设置情感体验的情景，激活学生的思维。 【教学片段】牛津初中英语初三上册第四单元的阅读文
雅思口语话题part2参考范文 学习模仿雅思口语范文要点有3：1.native speaker的地道用词，2.丰富灵活的动词，3.高分句型并熟练背诵之。下面就和大家分享，来欣赏一下吧。 雅思口语话题part2参考范文：和朋友愉快聚餐 Describe a dinner you enjoyed with your friends. When and where you had the dinner Who you ate the dinner with What you ate And explain how you felt with this dinner 1.适用考生：全适用 2.主题：一次愉快的自助餐 3.故事线：最近和朋友们在城市夜餐厅吃自助餐。我大约在两周前吃了这顿饭，晚上吃自助餐。那实际上是一次聚会，和我的学校朋友们聚会，我没有长时间的见面。通过电话，我们讨论并计划一起吃饭，一起聚会，讨论我们，并亲自会见那些朋友。
我们选择了城市夜餐馆，因为它适合我们所有人，而且我们知道这家餐馆的食物质量很好。 雅思口语高分范文示例： Recently I ate buffet at City Night Restaurant with my friends. I had this meal about two weeks ago and we ate buffet in the evening time. That was actually a get together short of a party with my school friends whom I have not met for a long in person. Over the phone, we talked and planned to have a meal cum get together party together to discuss us and to meet those friends in person. We picked the City Night Restaurant because it is in a suitable location for all of us and we knew the food quality of this restaurant was good. We met at around 5 pm and we spent some good time together. Lots of things have changed and some of our friends have done many challenging things and we all enjoyed the stories and updated we gave each other. Four friends of mine from school came to this occasion and they were James, Jessica, Harper and Patrick (...or say names of your friends...) It was a gathering we had been planning for a long and as part of our meeting, we planned to have a great feast. From the very beginning, we planned to have met in a restaurant and not at home.
沈阳市三年级道德与法治上册第二单元《我们的学校》单元测试卷 小朋友，带上你一段时间的学习成果，一起来做个自我检测吧，相信你一定是最棒的！ 一、填空题 1 . 我国宪法保护公民享有的____，同时公民要履行宪法规定的____。 2 . 我们已经在学校生活了________年多。在这里，我们学习、________、________…学校的各个角落都留下了我们的________。 3 . 每年的（_______）月（_______）是教师节。 4 . 拉拉手，交（__________）。 5 . 因为校园里的所有工作人员的默默工作，我们的校园才更安全、舒适、美好。我们要（______）学校的每一位工作人员，（_______）她们的劳动。 6 . 老师不仅教我们知识，还引导我们如何（__________），我们的每一点进步都离不开老师的（__________）。 7 . 我们不仅喜欢校园里的一草一木，更关心学校的发展、_____。 二、判断题 8 . 学校的建设是校长和老师们操心的事，小学生没有必要对此提建议。（____） 9 . 县、乡两级人大代表换届选举是全体选民行使选举权的重要体现。（____） 10 . 我们不是在一个单一的集体中的，我们可能身处许多不同的集体。（__） 11 . 我们教室的投影仪坏了，我们要到学校教务处去找领导来解决。（______） 12 . 除了老师，我们不用尊重学校里其他的工作人员。（______） 三、简答题 13 . 我们该如何保护书本？ ____________________________________
14 . 你已经入校三年了，这三年里学校都有哪些变化？说一说。
知识经济时代，科学技术的发展在越来越大的程度上决定着世界上每个国家和民族的兴衰存亡，也将更深刻地影响着个体的认知和行为模式，因此应从小就重视培养学生良好的科学素养，学会用科学的思维方式解决自身学习、日常生活中遇到的问题。本文尝试用思维导图策略去融入小学科学教学，探讨其对教师教学成长及学生学习的影响，并对其效果进行分析和反思，从而提供了小学科学教学中运用思维导图策略的教学案例和一些经验。一、思维导图融入小学科学课的教学理念近十几年世界各国都加大了科学课程改革的力度，我国科学课程改革是以培养科学素养为宗旨。早期的科学教育对于科学素养的形成具有决定性的作用。小学科学是一门形象性很强的学科，瑰丽多彩的自然现象，生动直观的实验，让小学生在从直观现象、感性知觉到逻辑思维、理性分析的质的飞跃的学习过程中遇到了很大的困难，如何在两者之间铺设自然过渡的台阶，提高教学成效?笔者认为由托尼巴赞所提出的思维导图是个可行的策略，它是一种视觉组织工具，其知识表征方式及过程、对知识的表达与理解，与科学教学有其共通之处，可让学生更容易掌握科学知识结构，理解其抽象概念，提升逻辑思维能力，加强记忆能力。二、思维导图融入小学科学课的教学实践笔者与南京某小学三年级的科学课教师合作，针对科学课程的特点，参考思维导图的相关理论和实验研究，设计并实施了4 节课的科学探究课，通过教师教学示范，逐步引导学生，强调合作学习，以提升科学学习的效果。（一）教学设计（二）教学过程1．思维导图的学习思维导图是对发散性思维的表达，它结合了左右脑功能，能促进思考、记忆、分析及触发灵感。它对学生而言是新的学习策略，所以在学习阶段，采用4-6 人分组的方式来合作完成思维导图，以减轻学生的认知负担。练习的时候，教师要强调画图步骤与重点：主题放在中央，尽量加上彩色图案突显主题；将次概念放在次要支干上；每一个分支只写一个关键字，字体要端正；关键字要写在支干线条上面；结合符号、图画或色彩让导图更丰富；运用交叉联结不同分支的概念。本课中首先用学生熟悉的童话故事龟兔赛跑为例，告诉学生兔子、乌龟、赛跑是主要概念，看、爬为次要概念，结合植物的分类（图1）和假期计划（图2）两张范图的展示，引导学生由认识概念与联结语开始，培养他们逐渐分辨不同层次的能力，让学生对其绘制流程有初步认识，等同学对其绘制逐渐了解时，再鼓励同学独立完成思维导图，为下一阶段学习做好准备。2．主题的引入本课主题是地球上的水，教师将向学生讲授地球上的水的分类、比例，补充一些学生并不具备或不一定能够想到的知识，如：水的物理性质、物态变化等。同时对学生进行一定的引导，如人类为什么总聚居在河流沿岸等，引发学生的探究兴趣。由于关于水的主题可以从很多个角度去研究，这就需要教师帮助学生确立一个最基本的探究方向。本课中，教师将水的主题分为水的重要性、水的节约、水污染、水文化四个分支，对学生进行分组，合作学习。3．小组的合作各小组分好后，先要对作品内容进行设计。大家要商量从哪几个方面来探究选定的主题，再进行分工，保证每个成员都有任务。然后通过浏览网站，看书等方式收集相关资料并进行分类和整理，由于之前基本已掌握构图理念和技巧，可以独立完成思维导图的制作，学生在上课的时候将导图作品带到学校来，小组成员相互探讨其概念之间的关联是否准确，内容是否完整，修改不足之处，共同构建小组导图。在这过程中，每个同学的思维和知识会得到很好的展示，通过讨论，学生之间不仅增强了小组意识，而且容易得到肯定和发现自己的不足。例如同学在进行水污染主题的研究中，列举了会出现传染病、生物死亡、植被破坏等问题，在这些概念中有没有上层的或比较普遍的概念呢?显然，危害这个概念包含了上述3 个概念，应是主要概念和层次，通过老师的适当的引导和同学的讨论，学生逐渐理解主要概念和层次的分辨。4．成果的展示各小组将完成的思维导图向其他小组展示，在此过程中，其它小组的同学可以向该组同学提问，发表自己的意见和看法，对其作品进行一定的修订。教师在此过程中加入适当的引导和建议，最终同学们将研究的主题汇总，共同完成一个较理想的思维导图。通过同学们作品的展示，学生不仅学到本小组主题的相关内容和概念，也了解了同一主题不同角度的相关内容，使学生通过
雅思口语---介绍顺德碧桂园业主论坛https://www.doczj.com/doc/b11222591.html,/bbs.php 描述一个人 1. describe a person you once helped Why and how can parents teach children to help others? How can school teach students to help others? Do you like helping others? Why? What should you do when you help children/elderly people? Why do you think people want to choose careers that can help others, e.g. nurse, teacher? /Why should teachers and doctors help others? 2. describe a teacher you want to meet again What should the teacher do if the students dislike him or her? Why do you think some people want to become teachers? How can the government encourage the elite to become teachers? How are teachers paid now? What are the characteristics of good teachers? Some people think education is less popular than before, do you think so? Nowadays, how are teachers different from teachers in the past? 3. describe a foreign friend Can people you admire be good examples for you? In what areas can a good example influence you? What kind of young people can become successful? How does technology affect communication? What’s more important, educational background or experience? What do you think of friendship? What is the most important part of a friendship? Should friends share the same interests? Why? Do you think it’s better for friends to have different interests? What factors can break up friendships? Do you make friends who are much older or younger than you? 4. Describe a respectful family member What’s the difference between big families and small families? How does your family make big decisions? Who has the final say in your family? What kind of people can be successful? Can you define success? Why do you think people want to be successful? Why do people always link success and money together? How can one balance between work and life? 5. describe an adventurous person
高中生英语阅读教学论文如何运用趣味教学 概要：趣味教学在高中英语阅读教学中有着良好的运用前景，有助于提升教学 效率。从导读教学、现代教育技术、游戏情境、趣味结尾等方面入手，对学生起到了调动求知欲、激发学习兴趣、培养想象能力、强化应用能力的作用。自古以来，“教学有法，教无定法”，高中英语趣味阅读教学的研究并非一朝一夕之功，今后仍需广大教师不断地学习、积累与反思，尝试提出具有创新性的教学举措，为增加英语阅读课堂的趣味性和调动学生学习英语积极性而不懈努力。 为了有效运用趣味教学法，需要为学生营造良好的学习环境，引导学生主动探 索新知识，自主参与阅读教学。教师要根据高中生的特点，为学生预留习题做导读练习，在课堂上结合小组合作等形式為学生营造自主、合作的学习环境。现代教育学理论认为，教学并不是向学生填充知识，而是要点燃学生的思维，这要求教师注重学生兴趣的激发。孔子说过，知之者不如好之者，好之者不如乐之者，可见兴趣对于学生学习拥有至关重要的作用。因此，教师要重视营造了一个良好的教学环境，比如借助巧妙有趣的导读激发学生的参与热情，引导学生通读文本、探究词义，在轻松愉悦的氛围中，在自主合作的状态下良好的完成学习。 例如，我在教学中通常会选取能够最大化吸引学生兴趣的内容进行教学，比如 选用一段富有文采的片段让学生阅读。“Spring is fine. It's usually sunrry and warm. Sometimes it's windy and rainy…”在这样的一段素材中，我会要求学生带着感情去朗读，先对Spring有一个整体的认识，再对词义、句子结构有所 把握。这样的文本内容朗读起来有一种阅读朱自清先生散文的感觉，不仅句子流畅朗朗上口，更是富有情感，能够让学生自然地顺着阅读材料了解新的知识。 一、利用现代教育技术，激发学生的学习热情 随着现代信息技术的发展，为教育教学提供了诸多的帮助，现代教育技术越来 越广泛地应用在教学工作的各个方面。在高中英语阅读教学中，教师需要注重对学生进行技巧指导，要求在阅读中掌握关键词、句的信息，实现精读效率的提升。因此，教师可以借助多媒体等现代教育技术手段，摒弃传统灌输式教学模式的弊端，用直观、形象、生动的方式展现给学生，借助图形、声音、视频等手段与传统教学媒体有机结合，激发学生的学习热情。在现代教育技术手段的支持下，学生可以拉近与教材的距离，直观地认知感受新的知识。例如，借助展示视频或播放音乐，可以让学生自然地感知英语语言的魅力，热情地投入到英语学习中去。 在实际教学中，我们可以利用英文歌曲来营造英语氛围。如收集一些旋律优美、难度适中、与阅读教学内容相关的英文歌曲，在课堂和课余时间与学生们一起欣赏，
雅思口语话题part2高分范文分享 备考雅思口语需多看优秀范文，今天了雅思口语part2新题范文，希望能够帮助到大家，下面就和大家分享，来欣赏一下吧。 Describe a public place that you think need improvements You should say: What the place is When do you go thereWhat do you like and dislike in the public place 1-4月雅思口语新题part2范文之：改进公共设施(public place that you think need improvements) The public place that I think need improvements is the munity library near my home. I often go there after work, probably twice a week. There are several aspects I like about the library. The first one is the whole atmosphere. Every time I go into the
library, I can see people in all ages reading books, surfing in the inter, discussing questions, or watching movies, which makes me feel peaceful. The second one is the variety of books. It not only has Chinese books but also some English books. The third one is the easy aess to all the materials. Application to the library card is easy and free. People can borrow 6 books for one time. However, there are some parts I don't like about the library. For example, the library doesn't have a quiet study area. It's pretty noisy for people or students who want to study lonely and quietly. The library can consider to build a quiet study room in the future. Describe a city or country you most want to live in the future You should say: What is it Where is it located
Describe a TV presenter Well, there are so many different TV programs on show everyday in diversified channels; hence there are thousands and thousands of TV presenters we can see. Some may leave us a very deep impression while others may not leave a thing in our mind. Here, I’d like to talk about the TV presenter I love, Wuzongxian, although others may h ave a mixed feeling about him. Being a TV presenter in Taiwan, Wu started his career in the field of variety shows in the late 1980s, and then he became famous because of his unique style of hosting TV programs and therefore the TV programs he hosted are constantly ahead in the ratings. All these earn him the top salary among those TV presenters in Taiwan, being the local king for many rs. In 2008, he even won the Golden Bell Awards in television industry. His fast reaction and tactfulness in hosting is among the first class, second to few others, and gradually, he formed his own style of hosting TV programs. Because he likes to use those popular words on the internet, and his figure is not like other
中学英语阅读教学中学生语篇意识的培养和研究 摘要：想要提升初中学生的英语阅读能力，那么初中英语教师就必须在阅读教 学中培养学生的语篇意识。学生只有具备了语篇意识，他们才会在阅读和分析英 语文章的过程中得心应手。可以说通过在初中英语阅读教学中培养学生的语篇意 识能够从根本上提升学生的英语阅读理解能力。 关键词：初中英语阅读教学；语篇意识；培养策略 1语篇的概念 笔者认为语篇是由一个以上的句子或者语段组成的，具有内在衔接性和连贯性、符合一定的语言使用规则，并且可以表达某种意义功能的语言整体。以此对 应初中阶段所使用的英语教学材料（教学书），其中的对话材料、听力材料、阅 读材料、写作材料以及语法材料等都是以语篇为形式的。 2在初中英语阅读教学中应用语篇分析的必要性 2.1英语课程标准对“语篇知识”的要求 英语课程标准明确将“语篇类型”作为英语课程内容的六要素之一，并提出初 中英语教学需要培养学生的“语篇知识”。其中“语篇类型”指的是议论文、说明文、记叙文、议论文等不同类型的文体，不同类型的语篇具有不同的行文结构、文体 特征和表达方式。学习和掌握各种语篇的文体，能够加深学生对语篇意义的理解。《课标》中“语篇类型”与“语篇知识”的相关内容对初中英语阅读教学提出了新的 要求，即教师需要改变传统以语言知识为中心的阅读教学观念，提升以语篇为单 位的教学意识，在教学中既要关注阅读文本的微观组织结构，又要关注其宏观组 织结构。因此，将语篇意识的培养引入初中英语阅读教学，对于落实初中英语课 程标准的相关要求是非常必要的。 2.2传统阅读教学方法的弊端日益凸显 传统的阅读教学方法以“语法讲解”和“课文翻译”为主，教师通常将语言知识 作为英语阅读教学的主要目标，把阅读课堂的大部分时间用于词汇与语法知识的 讲解、句子成分的分析等，对于阅读文本的处理相对比较粗糙，翻译句子是其主 要的课文讲解方法。这种传统的阅读教学方法，看起来好像能够帮助学生分解阅 读理解的难点，降低阅读学习的难度，扎实地掌握词汇与语法知识。然而，这种 将阅读文本分解为“知识的碎片”进行讲解的方式，实际上不仅不利于提高学生的 综合语言运用能力，而且会导致语篇之于阅读教学的功能与价值的丧失，造成学 生不能从整体上把握与理解语篇的意义。长此以往，学生的阅读理解多停留在句 子层面，其阅读能力与思维能力的发展势必会受到一定的影响，最终也会影响阅 读教学与语言教学的实际效果。相对于以词汇和句子为中心的传统阅读教学方法 而言，基于语篇意识培养的阅读教学更注重阅读文本的整体性，强调从文本的宏 观与微观的双重视角进行充分的语篇分析，以提高学生阅读理解能力与综合语言 运用能力。 3初中英语阅读教学中学生语篇意识的培养 3.1注重语篇的多角度分析 语篇是初中英语阅读教学的基础性资源。语篇赋予语言学习以主题、内容和 情景，并以其特有的内在逻辑结构、语言形式和文体特征，组织与呈现信息，服 务于主题意义的表达。初中英语阅读教学应注重阅读语篇的多元解读，充分挖掘 语篇的潜在价值，开展丰富的阅读活动，帮助学生在整体理解语篇的基础上，把 握篇章的组织结构与逻辑形式;在分析主旨大意的基础上，领会作者的观点态度与
环球雅思口语老师给大家带来关于电影类的口语范文一篇，大家可以练习起来囖～ Do you like watching movies? / How often do you go to the cinema? Definitely, watching movies is my cup of tea. You know, I'm always busy in studying. But I will go to the cinema as often as possible, maybe once every other week because it is one of my favourite ways to relax. I believe films are a wonderful form of art. Many of my friends have the same habit, so we always go to Wanda International Cinema on weekends together, which I think is the best cinema in Beijing. Why do you like to watch films? Firstly, I think watching films is a good way to relax. You know, when watching a film, I will throw all of my emotion to the story so that I can forget nearly everything about myself, especially the troubles. Also, I can learn many things from the roles in films, for example, I learned how to join forces to fight again devil from my favourite movie . Do you like to watch films in the cinema or at home? I prefer watching films in the cinema, absolutely. You know, the atmosphere and the sound effect are prefect in the cinema, which you cannot feel at home, facing a small screen, especially for scientific and horror. So you can understand the story and the roles' emotion better, also you are able to learn more from the films. What kind of films do you like? I love fancy film the most. They are full of magic, imagination and creativity. I believe only imagination can make our real life more colorful. My favorite movie is , which is typical of this kind of movies. I admire J.K.Rowling, the producer of it; I really wonder how those wonderful ideas came from her head! Who is your favorite movie star? My favorite movie star is Yang Ziqiong, who is very famous not only in China but also in Hollywood. She is one of the earliest actresses accepted by Hollywood and became popular in the world. She's good at martial arts. She's not very beautiful but her kongfu is really amazing, quick, clear-cut and powerful. I think she's the proud of China, not only for her position in Hollywood, but also because she spread Chinese traditional kongfu in the world. What's the difference between American films and Chinese films? I think the biggest difference lies in the purpose of the film, result from the level of freedom. You know, most of Chinese films just describe an ordinary story and focus on teaching the audience some principles, because the government controls the contents of films stringently. But because of freedom, the purpose of most American films is to give audience an impressive shock, and then earn enormous profits.
道德与法制三年级上册第二单元《我们的学校》单元测试卷 姓名:________ 班级:________ 成绩:________ 小朋友，带上你一段时间的学习成果，一起来做个自我检测吧，相信你一定是最棒的！ 一、填空题 1 . 当老师的批评不够恰当，或者对我们产生了误会时，我们可以向老师说明________，也可以向老师提出________。提建议的时候要注意________，也要________老师。 2 . 《中华人民共和国宪法》第四条规定：“各民族都有（_____）和（_____）自己的语言文字的自由。” 3 . 学校有很多部门，负责教学工作的是（________），负责德育工作的（________），负责少先队工作的是（________），负责后勤工作的是（________）。 4 . 学校还有一些值得（______）的地方，只要我们（______）观察，都可以发现。 5 . 1985年（___________），是中华人民共和国成立以来第一个教师节。 6 . 在学校里，我们学习、活动、玩耍……学校的____________都留下了我们的足迹。 二、判断题 7 . 学校的建设是校长和老师们操心的事，小学生没有必要对此提建议。（____） 8 . 老师肩负着教书育人的重大责任。（______） 9 . 学习是自己的事，和老师、同学都无关。（______） 10 . 走出国门，每个人都代表中国，都是中国的名片，所以我们应该注意自己的一言一行。（______） 11 . 校长室只有老师才能进去。（______） 三、简答题 12 . 你已经入校三年了，这三年里学校都有哪些变化？说一说。 13 . 说一说你们学校和你原来所在的幼儿园有什么不同。
运用思维导图优化小学科学教学的实践研究课题方案
附件1： 编号 洞头县2015年教育科学研究课题 申报书 课题名称：运用思维导图优化小学科学教学的实践研究课题类别：□教科规划课题□教学研究课题 √教师小课题 研究方向：□(1)学校管理□(2)课程建设 √(3)学科教学与课堂变革 □(4)德育与心理健康□(5)体卫艺 □(6)评价与质量监测□(7) 教师教育 □(8)教育技术□(9)其它 课题负责人：电话（手机全号）： 职务或职称：√是□否县级骨干教师课题负责人单位： 洞头县教育科学规划领导小组办公室制
课题组成员的姓名 课题内分 工 工作单 位 职务或职称
有关情况（含负责人）
课题负责人所在单 位意见单位盖章： 负责人签字： 年 月日 县 级 初 审 单 位 意 见单位盖章： 负责人签字： 年月日 附：研究方案
课题 运用思维导图优化小学科学教学的实践研究 名称 研究背景（针对什么现象、问题或需求，研究价值或意义）500字以内 现象及需求：小学科学课堂教师、学生都很重视做笔记，但发现部分孩子被记录所“绑架”。有的动作慢，记这没记那；有的不会自己设计笔记，照抄老师的板书；有的抄后即忘，有笔记等于无笔记……导致科学课堂的低效及浪费。思维导图仅用关键词、图形和连线等可以把一节课、一个单元的知识甚至一本书、一门课程的内容“梳理”并“压缩”成由关键信息及其联系所组成的一张图，去除了冗余杂乱的信息，保留了关键内容。不仅便于加速资料的累积量，大大减轻了记忆的负担，更将资料依据彼此间的关联性分层分类管理，使资料的储存、管理及应用更加系统化，从而提高大脑运作的效率。。因此利用思维导图一方面能展示出思维的过程,另一方面有利于理清思维脉络，对于优化学生学习有一定的效果。 研究意义：本课题研究旨在通过分析国内外思维导图研究的基础上，依据现代学习理论、教学设计理论和新课程教育教学理念，探索在不同类型的科学教学中去运用思维导图，以及运用思维导图对学生认知结构、学习兴趣、学习思维的影响，以期能更有效地推进小学科学课程教学，并以此推广到其它学科，为三导式课堂模式的实现提供一些参考和借鉴。 研究设计（研究目标与预期成果、研究内容与方法）500字以内 研究的目的：1、使学生了解思维导图的起源、思维方法、应用制作方法；掌握绘制思维导图的手段；应用思维导图记笔记、写总结、汇报成果以改善学生学习科学的方式、激发学生的学习科学兴趣、培养学生的放射性思维能力，实现与新课改的接轨。 2、教师在教学过程中借助思维导图这一技术，通过对众多知识点的自由组合或建构多种方案，培养和训练学生的创新思维，树立全局的观念，提高教学效率以及深化教学方法的改革提供最有力的工具。
浅谈如何提初中学生英语阅读能力 英语阅读是外语教学中的重要一环，而提中学生英语阅读理解能力也一直是英语教学的重要目标。怎么提高中学生的英语阅读能力呢?本人觉得调动学生学习的主观能动性，激发学生的主体意识是关键。 那么作为老师我们应该怎样帮助学生提高阅读理解能力呢？在初中阅读课的教学中应如何调动学生学习的主观能动性，激发学生的主体意识，以达到培养和提高学生的英语阅读能力呢？我觉得要做到以下几点： 一、作为教师要充分了解影响阅读理解的因素 影响阅读理解的因素很多，包括语言知识、认知技巧、个人经历、世界知识等等。语言知识包括词汇、语法、语篇等不同层次，是理解语言的基础。读者在阅读时不能一个词一个词地理解，有效的阅读，应该更多地关注关键词或语意群。背景知识是影响阅读理解的另一个重要因素。学生的已有知识、经历、概念能力、理解模式、情感、情绪以及其他种种非理性因素构成了学生的认知图式，这些因素相互影响，从而使读者在阅读能力方面存在着差异。 二、作为教师要为学生设计好提问，激发学生的主体思维 问题设计是英语阅读教学的重要手段，是帮助学生理解、鉴赏文章的切入点。教师设计问题，要遵循循序渐近的原则，要把问题建筑在学生的注意力和兴趣之上，服务于全面提高学生素质水平的目标需要。 初中英语课文内容参透了丰富的审美以及科学教育内容。在这些兼顾知识性、趣味性、思想性的阅读课教学中精心设计既有启发性又能激起学生探讨兴趣的系列问题，可以启迪学生动脑，激发学生思维，培养学生主动学习的习惯。 通过这样的提问，推想出课文中没有写到但又与之关系密切的若干内容，能使学生加深和拓宽对课文人物刻画、主题表现的领悟，激起他们去主动思考和探索。