Sergei Abdamovich, galina Nikitina, Vladimir Romanenko

Developing Practical Competence of Future Engineers within a Theory-oriented Curriculum at the Tertiary Level

This paper focuses on skills required for the background of an engineer and investigates processes that shape the development of such skills during one’s freshman and sophomore years. A list of basic engineering skills determined through the series of ‘converging’ surveys is suggested and several teaching strategies that positively affect the development of the skills at the tertiary level are introduced. The quantitative part of this study is based on data collected from about 19,000 subjects over a 15-year span. In particular, it was found that technology makes it possible to overcome a tension between the ease and the challenge in the context of general physics coursework by providing future engineers with computerized interactive assignments of alterable complexity.

Страницы: (2002) v. 4, pp 24-30


It has been a long established intellectual tradition that the process of education at the tertiary level concentrates mainly on the creation of theoretical knowledge of a particular profession (B. Cohen, 1985; D.Bridges, 1993; A. Booth, 2001). That may be a reason why successful experience in university education does not guarantee the acquisition of skills ensuring one’s practical competence (D. Bridges, 1993). For example, an engineering student well educated in mathematics and science does not necessarily possess skills relevant to the world outside the boundaries of formal university education in these areas. On the other hand, a focus on the development of skills as an enterprise in vocational training at the expense of teaching theoretical foundations of a particular profession raised opposite concerns and anxieties (A. Booth, 2001). Nonetheless, the process of university teaching brings about the formation of various important skills for the students. This paper examines the ways in which practical competence of future engineers develops through education at the tertiary level during freshman and sophomore years.

How do future engineers develop skills through the study of theoretical disciplines? What skills are important for a successful career of an engineer? With the goal to answer these questions, three levels of skills that develop at the tertiary level will be singled out in this paper. The first level includes skills used by an individual in analyzing problems, making connections among different ideas and concepts, introducing new hypotheses, distinguishing among a variety of factors and eliminating those of a random nature. Such higher order skills characterize human creativity in general and therefore they are not content-specific. In other words, once developed, these skills constitute what may be referred to as generic competence of creative individuals working within content areas as diverse as biology, education, civil engineering, and semiconductor materials research to mention just a few examples. Usually, these higher order skills are not explicitly taught even at the tertiary level.

Skills associated with content specific professional abilities belong to the second level. Such skills are different for different professions and develop mostly through explicit transmission of theoretical and knowledge base of a particular profession (B. Cohen, 1985). For example, skills and abilities of a civil engineer are quite distinct from those required from a semiconductor materials engineer. Nonetheless, despite the apparent diversity of the second level skills, one can recognize several commonalties that underlie them. Indeed, there exist skills and abilities common to the variety of fields that deal with the estimation of errors, interpreting information presented in different but traditional notations, systematizing results of experiments, etc. Apparently, such routine skills are transferable (D. Bridges, 1993; I.Kemp & L. Seagrawes, 1995) in the sense that they are applicable more than to one context. These skills, referred to below as engineering skills, constitute the third level in the classification suggested in this paper.

Hidden curriculum as a vehicle for skills development

Whereas some skills, regardless of their level, develop in the course of the study of a particular subject matter – mathematics, physics, chemistry, etc., there are skills such as a proficiency in using a computer, calculator, handbook, index, table, dictionary, and thesaurus that have essentially an interdisciplinary nature. Not explicitly taught, such cross-curricular skills emerge as a part of a learner’s repertoire through his or her active participation in the master/apprentice type of a learning environment that encourage direct engagement in the discovery of knowledge (A.Booth, 2001). Often in a lab setting, the instructor-student relationship moves quickly from the expert/novice plane to the master/apprentice plane (S. Abramovich, 2000) and that type of educational dynamics obscures actual processes responsible for the development of the above-mentioned cross-curricular skills. In other words, one can identify skills that develop through what can be conceptualized as hidden curriculum of engineering education.

The concept of hidden curriculum can be traced more than three decades back when its significance for learning at the pre-college level was first acknowledged (N. V.Overly, 1970). The very term ‘hidden curriculum’ was coined by P.V. Jackson (1968) in connection with tacit features that structure life in schools.  Many hidden messages of pre-college curriculum, which might not have been deliberately hidden (E. Vallance, 1983), enable students to learn skills needed to maintain social order. Such a functionalist perspective on hidden curriculum (A. Skelton, 1997) concerns primarily with social relationships within a learning environment structured by gender, socio, economic, and cultural background of the students. Recently, the concept of hidden curriculum has been explored in connection with using technology in mathematics teacher education (S. Abramovich & P. Brouwer, in press) as “a way to show the teacher the places where the learner might step into the learning process of mankind” (H. Freudenthal, 1983, p. ix).

This paper investigates hidden educational processes that are responsible for the development of engineering skills of freshmen and sophomores at the tertiary level.  While at that level the impact of socio, economic, cultural, and gender factors is somewhat moderate, new educationally significant factors, such as cooperative learning, reading subsidiary literature, and surfing the Internet must be taken into account if one attempts to explore the processes in connection with learnings at undergraduate level. Enhanced by hidden curricular messages, the learnings not only foster theoretical and knowledge base of an engineering profession but, better still, nurture engineering skills of the students. Unlike the elementary and secondary levels, the development of skills through hidden curriculum at the tertiary level can be controlled through the appropriate instruction.

Delphi method and its modification

The first task in the described study was to determine skills that develop through hidden engineering curriculum during freshman and sophomore years. A standard approach to such a task is to poll different groups of experts. A major problem associated with the polling approach is to develop an accurate list of questions used as a polling tool (K. Klauer, 1996; M.S. Lemos, 1996). An alternative approach is not to use list of questions as a polling tool but rather to create such a list through a polling process. In other words, the task was to develop a list of questions through the series of ‘converging’ surveys. To this end, the Delphi method  (R.U. Ayers, 1969) commonly used in the area of scientific predictions was administered. A group of experts consisting of university faculty (N=8), industrial researchers with advanced degrees (N=9), and practicing engineers (N=15) was asked to create a full list of skills essential for an engineering profession. It took the group three months to complete the task.  As a result, each expert came up with his or her own list of responses presented in a loose format. According to the Delphi method, all responses were analyzed and skills common to all the lists were identified and recorded in a final list.

Drawing on the ideas of Yu. V.Chaikovsky (1990), the Delphi method was then modified to allow for more than one group of experts to be involved. To this end, the second group of ten experts was given the above final list with the following inquiry: Are the skills that were found by the first group indeed important for one’s engineering background? In addition, the experts of the second group were encouraged to make changes in the list according to the group’s opinion. In such a way, the second list was created.

The modified Delphi method was administered several times enabling the lists to converge.  In other words, this recurrent procedure with eight to ten experts involved in each (additional) group generated two identical lists of skills that are essential for one’s engineering background. Multiple application of the modified Delphi method resulted in the following finding: After three steps the procedure converges to the (final) list of 20 engineering skills that develop through hidden curriculum of engineering education during freshman and sophomore years.

Once the final list was created, each member of the last group of experts was asked to rank all skills in the list according to their importance. The skills were then arranged in the decreasing order of the sums of their ranks given by the experts. Table 1 shows such an arrangement of the skills. It should be noted, however, that the arrangement of skills in Table 1 may be subject to change in accordance with advances in the development of information technology.

Table 1. Arrangement of skills in the order of importance.

  1. Systematizing and recording of observed and experimental results.
  2. Classification of data.
  3. Judgment of statistical characteristics of a result and its reliability.
  4. Graphing as data representation.
  5. Appropriate and in-scale representation of results through diagrams and graphs.
  6. Execution of simple calculations.
  7. Conducting numerical analysis and graph-based problem solving.
  8. The use of nomogramms.
  9. Discovering of outliers.
  10. Smoothing curves over and graphical differentiation/integration.
  11. Computer programming and the use of computing technology.
  12. Statistical layout of an experiment.
  13. Assessment of peculiarities of a measuring unit.
  14. The use of the method of dimensionality.
  15. Conducting literature search.
  16. Appropriate keeping references on file.
  17. The use of up-to-date retrieval systems.
  18. Summary writing.
  19. Competent use of Library of Congress cataloging-in-publication data.
  20. Unambiguous formulation of task objective.

The study of mistakes and misconceptions

Typical mistakes and common misconceptions by students were the focus of the next study presented in this paper. More specifically, the study of misconception that caused mistakes made by freshmen and sophomores in lab assignments, write-ups, tests, and exams constituted a new domain of inquiry. Instructors from five universities of the former Soviet Union with similar engineering programs participated in this investigation. Careful identification of mistakes in all of the types of the students’ work by the instructors enabled the collection of data. First, mistakes related to lab assignments within a specific course were studied. The span of this study was the same for all universities involved.

All mistakes discovered in lab assignments of a particular course were divided into several basic categories suggested yet by another group of experts. This group consisted of the members of science departments of different universities. Each expert had at least 10 years of teaching experience in his or her field. Basic categories suggested by the experts were skill-related. In order to evaluate an extent to which the arrangement of mistakes into the categories was relevant, the results of different experts were compared using the concordance coefficient. This coefficient was calculated using a standard statistical method.

At one university the study focused on the dynamics of the variation of students’ mistakes over a course span as the function of a learning environment created by a particular instructor. For a fixed number of mistakes/misconceptions related to class assignments and final exams, such dynamic was explored across different categories as well as within one category out of those suggested by the experts. That made it possible to assess the efficiency of teaching and learning for a particular course.

It was discovered that some mistakes/misconceptions have been typical ones as they were uniformly distributed across different subject matters. Most of such mistakes have been due to the erroneous interpretation and systematization of results, incorrect methods of computing, and tendency to misconstrue the applicability of a particular experimental and/or computational approach.  It is worth mentioning that subsequently, masters theses, doctoral (Ph. D) dissertations, and even university publications were found to contain such mistakes and misconceptions.
Establishing relationship between the skills and the mistakes

The list of so discovered mistakes and misconceptions was then compared with the list of skills obtained through the modified Delphi method and all items in the former list corresponding to those in the latter list were recorded.  Such a subset of the list of mistakes and misconceptions has come into the focus of a new inquiry. In other words, establishing a relationship between the skills and the mistakes made it possible to focus on those skills and abilities that belong to the core of one’s engineering background developed during freshman and sophomore years with the help of hidden educational processes. The main interest concentrated on students’ performances related to practices at physics labs because the form of such assignments in the study of physics is, in fact, common to all technical universities, community colleges, and professional schools.

It appeared that mistakes and misconceptions demonstrated by future engineers stem from the lack of those skills and abilities which are not the part of a conventional instruction. The understanding of this type of educational dynamics allowed for the creation of several alternative instructional strategies aimed at the empowering of future engineers with many important skills and abilities undeveloped within a traditional theory-oriented curriculum.

In addition, the number of fundamental innovations that underpinned the alternative instruction is worth noting. These innovations dealt with the emergence of a new type of classroom discourse due to specially designed lab assignments. More specifically, a variety of the assignments were extended to encourage reflective inquiry based on the same set of questions. For example:

What are the principal errors of experimental methods involved in an assignment?

What recommendations regarding a possible improvement of the method involved could be given?

What new methods are appropriate for the measuring of parameters involved?

This type of a lab-based instructional discourse was suggested earlier by P.V. Ovcharenko (1990).

Assessment of the educational innovation

The assessment of the efficiency of the alternative educational strategies was based on the use of the so-called index of mastery of a test defined as the ratio of correct answers to the total number of answers sought on the test (V.P. Bespal’ko, 1977). It has been due to the contribution of quite a few volunteers, mainly university professors, that such an assessment was conducted. An experimental group consisted of about 800 students taught through the alternative educational strategies; a control group consisted of about 450 students taught in a traditional way. Both groups were given the same tests during an assessment period. The dependence of the index of mastery on time had been studied over a 15-year span. This made it possible to collect data from about 19, 000 student interviews.

On average, in the span of fifteen years, the data indicated that a percentage of male students in different groups varied in the range 45%-50%. In particular 40% of male students were in the 18 to 25-year range, 55% – in the 26 to 30-year range, and only 5% were over 30-year old. No correlation between gender/age and educational growth of the students had been found. From the study of some interviews, coherence between the creativity of a student and the cultural level of his or her parents had emerged. This statement, however, can not be substantiated at present and the authors plan to undertake a separate study in this direction.

The graphs in Figure 1 show how the index of mastery for both groups changes over time. Each point on the upper (lower) graph represents the index of mastery averaged for 800 students in the experimental group (450 students in the control group) during a corresponding year of study. The graphs were plotted under the assumption of being logistic curves (R. Ayers, 1969;  G.V. Nikitina & V.N. Romanenko, 1992). Overall, as the graphs show, the alternative educational strategies have proven to be a success.

It is worth noting that the graphs in Figure 1 have similar shapes across the groups of students having different educational background and level of creative performance.

In addition, students with different levels of creativity may have different levels of the internalization of knowledge over the time span. Another interesting finding is that the curves in Figure 1 perfectly match those presented by Ya.A.Ponomarev (1976) and R.V.Weisberg (1999). Finally, students of different abilities may have different rates of

Fig. 1. Graphs of the index of mastery for the experimmental and control groups.

knowledge acquisition. In other words, the interim character of the internalization of knowledge scarcely depends on creative skills, age, and educational background of an individual.
Towards new educational scenarios in the preparation of engineers

In addition, the study focused on students’ performances on a number of specially designed non-traditional assignments that included several educational innovations in the study of an experiment. A rationale for a non-traditional assignment was different from that of an earlier (traditional) assignment. For example, in one such new scenario the students were asked to (i) evaluate the accuracy of an experimental method involved; (ii) find values of acceptable errors, (iii) choose a method of computing, and (iv) systematize

experimental results. It is worth noting that all these additional assignments, including those computer-based (briefly discussed in the next section), dealt mostly with the development of skills pertaining to engineering background. The description of all such assignments can be found elsewhere (G.V.Nikitina & V.N.Romanenko, 1992)

Computer as a learning environment

One could not expect students to investigate and measure all outcomes resulting from the change of the parameters of an experimental process. A number of reasons, and a limited time in the first place, constrained students’ attempts to complete all investigations and explorations that one may consider relevant to a particular experiment. In order to overcome the educational deficiency of a traditional lab setting, it was suggested to enhance learning by using computing technology. The intent was to use a computer as a support system in the study of fundamental theoretical ideas that underpin experimental techniques used for measuring physical characteristics of a process. To this end, a variety of technology-enabled assignments were designed.

The ease of computational experiments allowed the students to obtain data for a large number of experimental situations. Furthermore, through such experiments the students were able to observe patterns and make general conclusions that are not visible and are not reachable in a traditional, off-computer educational setting. Computational environments that made the modeling of experiments possible were structured by some very general relationships between the input and output of a physical process. The use of technology enabled a student to animate a studied process by any relevant parameter and observe interactively the dependence of the process on the variation of the parameter. In doing so, it has been a student’s responsibility to choose the conditions of an experiment and to control it from a keyboard.
Overcoming a tension between the ease and the challenge

The effectiveness of using computing technology as an instructional tool was assessed through student interviews. It was found that low-level requirements in computer-based tasks diminished students’ interest in the subject matter and discouraged their active engagement in computational experiments. In other words, the students perceived computer assignments as a worthy addition to traditional educational environments if a certain level of complexity of the assignments was provided.

The analysis of students’ performances within the on-computer setting indicated that both easy and difficult computer-based assignments have been detrimental to effective instruction. On the other hand, computer-based assignments with abstemious requirements were not effective either for they impeded any challenging instruction. Such an educational tension was discovered through the following approach.

It has been a long tradition at Russian universities to schedule the study of general physics as a two-semester course sequence taught as a large lecture class. Such a class then splits into a number of discussion sections that meet separately in a lab, seminar, and oral exam settings. The two-semester instructional sequence did not include the use of computers during the first semester. Results from this semester’s final exam were used to divide all the sections into three groups according to a section’s average grade.

As the teaching of physics continued into the next semester, the above mentioned computer-based assignments were the part of the instruction. There was an assumption that the use of a computer does affect students’ learning of physics in general and their results on the final exam in particular. With that in mind, the results of the first and second exam sessions were then compared. It came at no surprise that the introduction of computer-based assignments with abstemious requirements into instructional process decreased the number of low grades across the three groups. However, it has been an unexpected finding that the use of technology decreased the number of top grades across these groups also.

In order to avoid the decrease of top grades as a result of using technology, computer-based assignments with different levels of complexity were designed. It was a computer that automatically moved a student to a higher level after he or she achieved a success at a lower level. Another major problem was to create assignments of different but uniform complexity across the assignments and the polling of instructors was helpful in solving this problem. In addition, it was necessary to find the optimal number of correct responses that prompts a computer to change the level of complexity of an assignment. This approach enabled all students to keep an enduring interest in active work on a computer environment.
Revising the list of skills and improving technology-enhanced didactics

The set of skills required to keep up one’s professional expertise is subject to change as a society develops and improves its information technology. That is why the best way of improving educational technologies discussed in this paper is to revise the list of skills periodically. It is the revision of the list rather than its augmentation that bears significance because skills and know-how of an individual can not grow through the process of education infinitely.

It appears that the list of skills presented in Table 1 may be subject to revision. The revision of such a list should be based on the same approach as was used to create it and, if possible, carried out by the same experts that were involved in making earlier recommendations. Several changes that occurred in the list made it possible to suggest specific changes that should be incorporated into educational technologies through the process of revision. In such a way, continual surveys based on the techniques described in above appear to be helpful in improving educational technologies over the time span. Different ways of such an improvement of the technology-enhanced didactics have been studied in (V.N. Romanenko, G.V.Nikitina, and P.V. Ovcharenko, 1998; V.N.Romanenko and G.V.Nikitina, 2000). Note that a similar study was conducted earlier by K. Klauer (1996).


In this paper the authors proposed the classification and ranking of skills that are essential for one’s engineering background. The list of twenty skills that develop through hidden curriculum of engineering education has been contrived using a modification of the Delphi method in the form of ‘converging’ surveys. A relationship between the skills and common mistakes and misconceptions demonstrated by future engineers has been established. In particular, it was found that these mistakes and misconceptions stem from the lack of skills and abilities which are not the part of a conventional instruction. Alternative educational strategies that positively affect the development of the skills and enable one to overcome these misconceptions have been suggested. It has been argued that the development of the skills could be improved through the appropriate use of computers. In particular, a multi-level computer-based assessment was found to be an effective instructional tool when used with advanced students. The development of the skills over time was described graphically using a logistic curve. A theoretical underpinning of using a logistic curve in the context of the development of engineering skills through hidden curriculum is a topic of a separate study by the authors.

Pavel V.Ovcharenko of the Murmansk College of Civil Engineering provided significant help in the experimental part of this work. The authors owe him an immense debt.

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