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FEDERAL STATE AUTONOMOUS EDUCATIONAL INSTITUTION

HIGHER EDUCATION

"NATIONAL RESEARCH UNIVERSITY

"HIGH SCHOOL OF ECONOMICS"

Faculty of Social Sciences

Institute of Education

Graduation qualification work in the direction of training 38.04.02 "Management"

«Determinants of academic achievements in continuing education»

Student of the group No. 804

educational program "Management in Higher Education"

Zagorodnova Ekaterina Pavlovna

Scientific adviser:

Candidate of Economic Sciences, EA Shakina

Moscow 2017

Index

Abstract

Introduction

1. Academic achievements of adult students: discussion in academic literature

2. Emerging educational systems: common features and discrepancies with the developed markets

3. NRU HSE case: western academic traditions on Russian educational markets - pro and cons

4. Research design: from enrollment campaign to learning outcomes and academic achievements of adult students

5. Data and methodology: students' survey and tracking of academic achievements

6. Empirical results

Conclusion and discussion

References

Abstract

1. Academic achievements of adult students: discussion in academic literature

The concept of academic achievements has been widely studied in various scientific researches. However, these studies mostly consider academic achievements of students receiving professional education for the first time (Wigfield, Eccles, 2000, Cassady, Johnson, 2002, Licht, Dweck, 1984, Park, Kerr, 1990). A small numbers of studies pay attention to adult learners (Burrow, 2016, Ndlovu et al. 2013, Merriam, Caffarella, 2007). As we can see, the recent trend is considering adult students as a very important part of the educational system, so that the number of researchers who are turning their attention into this subject area is continuing to grow.

There is no commonly accepted definition of this phenomenon, as well as no universal methodology that allows us to identify the factors determining success or failure in academia. According to the Oxford Dictionary academic achievements represent students' performance that indicate their efforts to achieve particular purposes. The term “academic achievements” may refer to a large variety of educational outcomes, that is why the definition depends on the characteristics used to measure it. The general understanding of academic achievements includes grades or other performance which may be evaluated by different testing systems.

Academic achievement theory development gave rise to a larger number of methods to measure it, for example intelligence tests of Binet and Simon. (Boake, 2002) The basic sight into empirical and theoretical base of academic achievements determinants could be found in Woolfolk's (2007) works concerning educational psychology.

All scientific papers about academic achievements can be divided into groups. So that, several papers have focused mainly on selected determinants of academic achievements or on special predictors of them. According to them it is more appropriate to reveal predictors of academic achievements instead of determinants of them. The quality of teaching, in particular, has been emphasized as a predictor of student achievement. Large-scale scholastic achievement assessments provide an overview of the current state of research on academic achievement. From the scientific point of view, it is very important to compare different education systems on the international level. Moreover, this concept allows us to deal with this comparison and evaluates different educational systems with each other. However, it should be mentioned critically that this approach may, to some degree, overestimate the practical significance of differences between the countries. (Spinath, 2012).

Some researches paid increasing attention to the role of family background and family situations. A current work provides an overview of the empirical findings on academic achievement by distinguishing between individual, home, and scholastic determinants of academic achievement according to theoretical assumptions. (Hattie, 2009) The individual's performance correlates with university students' performance on the whole. The importance of academic achievements may be considered in different perspectives, for example for individuals and societies. (Richardson, et al. 2012)

The prediction and the analysis of the students' academic performance is not an easy one. Based on the definition of academic achievement that we will be referring to in this paper, motivation plays an important role with which a person comes to the educational institution. In this connection the concept of school education, university education as well as adult education in order to obtain additional skills should be distinguished.

Adult students or adult learners are the certain category of people, who requires an individual approach. Adult learners are persons who are older than 25 years and involved in the learning process. Usually scientists identify them as more developed students.

But we cannot say that only age determines whether a person becomes an adult student or not. There are some characteristics of mature students according to which we can identify this category. The following psychosocial factors can provide a portrait of the adult student:

· time of enrollment - usually time lag when entering the new education;

· full time employment (more than 35 hours per week);

· financially independent person;

· may have dependents - child, parents, spouse or others;

· psychological aspects of learners such as energy, health, vision and hearing;

· personality and cognition.

One of the basic approaches to building the concept of adult learning is the basis that this category of people is self-directed and self-motivated. The ordinary, mature student can be described as a person who is ready to learn and obtain new competencies. He/she has already recognized the need for new skills and knowledge. This person wants to use his time and money in a more productive way, because he/she considers them as the most valuable resource. (Knowles, 2002) Adults have some connections with their family, friends, work and community. They do not want to lose them just because of University enrollment. (Lakin, 2009)

As we said earlier, mature students are self-directed and self-motivated. It means that adult learners are independent of others and can estimate for themselves their progress in the training field. In view of the fact that they have self-motivation, it is easy enough to compare their expectations with the skills they actually receive from the educational process. Nowadays the trend is that adult students are learning not only for the technical skills, but also for communication and experience exchange. (Kidd, 1973) Because of this, returning adults need to have enough variety of education programs to meet their expectations in a closer way. (Merriam, Caffarella, Baumgartner, 2007)

According to motivation for continuing education, adult students are characterized by their ability to problem-centered approach to thinking. Moreover, experience in life skills should not be excluded. Some mistakes that occur may be transferred into opportunities for new learning. (Kidd, 1973)

Table 1. Determinants of academic achievements in different groups of researches

The first group of research concluded that gender, marital status, work experience and age influence academic achievement while faculty of graduation and internet use per day are not statistically significant. Ndlovu, Moyo, 2013 found that age and marital status do affect academic achievements, while financial status does not influence academic performance. Attendance, self-concept and learning style are very important factors for improving academic performance and have a positive correlation with achievements.

To sum up, the above mentioned determinants of academic achievements were studied mainly in developed academic markets. However can we assume the same for the emerging higher education system of Russia? And Perm region, specifically? This study aims at discovering the specific drivers of academic achievements of adult students under specific conditions. For that we explore the case of HSE in Perm educational programs.

All of these studies have something in common and help to look at the problem of academic achievement from two points of view: in terms of socio-demographic characteristics, and in terms of behavior characteristics.

2. Emerging educational systems: common features and discrepancies with the developed markets

3. NRU HSE case: western academic traditions on Russian educational markets - pro and cons

The Higher School of Economics is a research university carrying out research, education, expert-analytical and socio-cultural activities based on the international scientific and organizational standards.

Since its founding in 1992, the National Research University - Higher School of Economics (HSE) has developed from an economics institute into a comprehensive university. HSE is composed of more than 20 faculties, including electronics, computer science, mathematics, communications, media, design, world economy and international affairs, and most recently physics. The HSE offers outstanding educational programs from secondary school to doctoral studies, with top departments and research centers in a number of international fields.

Shortly after its founding, the HSE began to offer continuing professional education programs for adults seeking advanced vocational training, occupational retraining, or a second degree. Programs are offered in business informatics, practical psychology, corporate governance, and construction and municipal services. The HSE's continuing education programs work closely with the university's faculties, research centers, and laboratories and they are taught by highly qualified instructors.

The HSE is based in Moscow, with branches in St. Petersburg, Nizhny Novgorod, and Perm. The Higher School of Economics in Perm was established in 1997. At that time the Higher School of Economics was a very young university with no more than 2,000 students. The main objective was to launch strong educational programs in economics, management, and later in business informatics and humanities. The educational programs in quantitative terms are presented in Table 2. In the initial stages of the development the Perm branch performed well at this task, having built one of the strongest bachelor programs in these areas.

Table 2. The dynamics of educational programs in the NRU HSE - Perm

All the administrative model of HSE in Perm, as well as in the University generally, built under the effective implementation of the educational function: built faculty-department organizational structure (Table 3) to attract the best professors within the Perm academic market. A retraining system for professors was organized in leading Western universities that allowed the HSE to implement the best practices for teaching economic disciplines.

Table 3. Structural changes in the educational departments of NRU HSE - Perm

In 2009 the University entered a new development stage. The University significantly expanded the range of its competences in the socio-humanitarian areas that changed its mission. Having the status of a research university, the HSE has committed itself to reach a qualitatively new level in their academic results. One indicator of this claim is an academic scientific activity indicator (table 4). Most importantly, educational programs have ceased to be the only "product" of the university. In addition to the educational programs of the HSE is the goal to produce high-quality basic and applied research on priority subjects. Perm campus as part of a distributed university embarked on a new direction of development.

Table 4. Academics scientific activity indicators of NRU HSE - Perm

Also HSE campus in Perm actively developed in the field of adult education programs. The main types of educational activities of the branch in this subject area include the implementation of:

· second higher education programs,

· upgrade qualification programs,

· occupational retraining programs,

· corporate trainings,

· seminars and trainings.

The number of programs and the number of alumnus for the adult education changes of the whole campus is presented in the table 5.

In 2015 Perm campus of the HSE implemented 84 programs for adult learners, 4 of which are second higher education programs, 69 of which are upgrade qualification programs, 15 of which are occupational retraining programs. Moreover, 21 short-terms programs were implemented in the campus (seminars, trainings, workshops).

Table 5. Number of programs and the number of alumnus for the adult education in NRU HSE - Perm*

In the last 4 years there has been a steady increase in demand for a second higher education program (figure 2).

Figure 2 Number of incoming persons

4. Research design: from enrollment campaign to learning outcomes and academic achievements of adult students

According to the problem statement of this research we have to reveal the specific factors of adult students to put effort, invest time and money in their continuing education. Moreover, we tend to identify drivers of better academic achievements. The empirical analysis is based on the first year academic performance of HSE students measured by Grade Point Average (GPA). The GPA is a commonly used indicator calculated by taking the number of grade points a student earned in a given period of time. For this study it is critically important to focus just on early academic achievements of adult students as we target initial drivers of their achievements, unrelated to their peer's effect and other factors that might be considered endogenous. The potential drivers of adult student motivation to study were found by a survey conducted during two admission campaigns in 2015 and 2016. The questionnaire was designed to reveal the following:

· Socio-demographic indicators: age, sex, marital status etc.

· Professional and educational experience: experience on the position, level and type of occupation, previous studies and university etc.

· Subjective perceptions of educational programs: programs of interest, reasons to choose particular programs, factors of quality that students find the most important etc.

· Financial factors: source of financing for educational programs, monthly income of a family (sized by a number of family members) etc.

This survey was conducted among all persons who applied to the HSE continuing educational programs in 2015 and 2016. Still, not all of them were finally enrolled in the programs. That led us to the censored data since academic achievements are observable only for those students that have been studied. To avoid self-selection bias brought by the nonrandom factors that selected students during admission campaigns we applied the Heckman two-step procedure for our estimations.

Heckman's model is one of the statistical methods that can be used to estimate determinants of academic achievements under conditions of censored data. As has been previously stated, statistical analyses based on non-randomly selected samples can lead to erroneous conclusions. Heckman suggests a special correction based on a two-step statistical approach that offers a means of correcting for non-randomly selected samples. The correction uses a control function idea and it is easy to implement. Heckman's correction involves a normality assumption, provides a test for sample selection bias and formula for bias corrected model. This model supposes that the functional form of the causal relationship between treatment, outcome and covariates is linear. (Heckman, J., 1979)

At the first stage a model based on economic theory should be formulated. At the second stage the researcher corrects for self-selection by incorporating a transformation of these predicted individual probabilities as an additional explanatory variable. (Heckman, Robb, 1986).

The econometric strategy based on Heckman correction procedure is introduced in the specification (formula 1.1 and 1.2).

In the first stage the following equation has to be considered:

,

where Enr indicates the fact of enrollment to the program (Enr = 1 if the respondent is enrolled and Enr = 0 otherwise), Z is a vector of explanatory variables that according to our educated guess effect a probability to be enrolled during an admission campaign, is a vector of unknown parameters - coefficients, and is the cumulative distribution function - probit in our case. Estimation of the model yields results that can be used to predict this employment probability for each individual.

On the second stage, we have to correct for self-selection by incorporating a transformation of these predicted individual probabilities as an additional explanatory variable. The main equation may be specified as a linear regression:

,

where GPA is an exploratory variable is caused according to our hypothesis by several factors measured by vector of X () CV- a vector of control variables. The key control variables are responsible for the intellectual, cognitive and other important abilities to study. It is measured by the GPA from the previous diploma. We moreover control for the education field, level of the previous education and university. The Heckman correction procedure led to one more explanatory variable - с is the correlation between unobserved determinants of propensity to be enrolled and unobserved determinants of academic achievements, is the standard deviation of probability to be enrolled, and is Heckman's lambda which is the inverse Mills ratio evaluated at . This equation demonstrates Heckman's insight that sample selection can be viewed as a form of omitted-variables bias, as conditional on both X and on it is as if the sample is randomly selected.

The next section introduces some descriptive statistics of our sample of students and applicants for HSE continuing educational programs.

5. Data and methodology: students' survey and tracking of academic achievements

Figure 4. Marital status of the respondents

It needs to be said that in 2015 the married respondents were 5,6% more than in 2016. Probably this is due to the fact that the average age of the respondents in 2015 was higher than in 2016. Also it may be assumed that in 2015 the number of people who were married was more due to the fact that this year the number of respondents who already had a higher education was more than in 2016 (fig. 5). Now young people prefer to start a family after receiving higher education.

Figure 5. The respondents level of education

More than a third of respondents (40,6 % - in 2015 year; 32,5% - 2016 year) work in the service sector. In second place is the production area in which are involved about 14% of the respondents both in 2015 and in 2016. In the public sector there is about 10% of respondents in 2015 and around 9% in 2016. It is noteworthy that in 2015 there were no students working in the field of agriculture. In 2016 only one person was from this sphere. Also, the applicants indicated that they are working in areas such as medicine, pharmaceuticals, IT, energy and trade.

The income level for more than half of the respondents both in 2015 and in 2016 was up to 30,000 rubles (Fig. 6). About 20% of respondents earn from 30,000 rubles to 40,000 rubles. A salary of more than 100,000 rubles was received by about 3% of respondents. It should be noted that the level of income in 2015 and 2016 are identical. The low income level may be due to the fact that the majority of respondents did not have a higher education at the survey time.

Figure 6. The respondents level of personal income in 2015 and 2016 years

Predominantly to pay for studying, applicants (more than 50% of respondents) plan from their own savings, as well as from the monthly family income (40% of respondents). It should be noted that young people under the age of 22 years with secondary vocational education (35% in 2015 and 48% in 2016) rely on financial support from their parents.

In answer to the question "Why did you decide to continue education" the majority of students (78% in 2015 and 82% in 2016) have indicated that they need to move up the career ladder. The second most popular reason is an extension of their own knowledge. This answer was given by the respondents about 77% of the time. "Establishing of new contacts" was the answer of 31% of respondents in 2015 and 24% in 2016. The least common cause was the answer "I was sent by the organization in which I work." In 2015 this reason has not a single respondent and in 2016 it was only 1.4% of respondents.

When choosing a university most of the respondents relied on the parent's opinion (fig. 7). This is due to the fact that the predominant parts of the respondents are young people under the age of 22 years having secondary professional education and living with their parents. Also, friends and the media have an impact on the formation of opinion about the University. These categories were marked mostly by older persons and those with higher education.

Figure 7. Respondents answers to question “Whose opinion is the most important to you when deciding on choosing the university?”

When choosing a university, the major factors for respondents were the quality of professors and staff (70% of respondents), as well as the university status (53% of respondents) and prestige (47% of respondents). Least of all the respondents pay attention to the location of the university and on those, who trained there (fig. 8).

Figure 8. Reasons for university selection

It should be noted that the majority of respondents indicated in the questionnaire their surname, first name and patronymic name that allowed us to trace their further trajectory: entered or not, what is the level of academic achievement.

6. Empirical results

Conclusion and discussion

This study addresses the question about drivers of academic achievement of adult students in continuing education programs. Based on the literature and survey data and empirical investigation of two NRU HSE admission campaigns has been carried out. We have come up with the problem of adult students with a guess that these cohort has specific incentives to perform better their education due to their professional experience, financial issues and maturity. As a result of an empirical investigation of applicants' socio-demographic factors and subjective perceptions and NRU HSE students' academic performance a number of findings should be emphasized.

As was revealed information on academic achievements has made our data censored due to self-selection bias. Heckman correction has enabled consistent estimates of parameters. Socio-demographic factors such as marital status, and professional and educational background influence probability to be enrolled in the program in admission campaigns. Reasons for applying to HSE programs have a strong influence on decision to study as that reflects students' original intentions. Meanwhile, these factors have failed to find empirical evidence for higher academic achievements. The most crucial drivers of academic achievements that have been set refer to financial reasons. Students with not very high and not very low incomes outperform those from two marginal classes of wealth. The source of financial support is also very important. Thus, those students that pay for the education from their own salary or income study relatively better.