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Tech Tally: Approaches to Assessing Technological Literacy 4 An Assessment Primer Although few, if any, assessments are available in this country for technological literacy, many good assessment tools have been developed in other areas, from reading and writing to science and mathematics. Indeed, over a period of many years, a number of principles and procedures have been developed for obtaining reliable results. Although assessing technological literacy has some special requirements, the general principles developed for assessments in other areas are applicable. Thus, a logical place to begin the development of an assessment of technological literacy is with a review of what has been learned. The overview of the field of assessments in this chapter lays the groundwork for the remainder of the report, which zeroes in on the assessment of technological literacy. The first section lays out the basics of testing and measurement—definitions, key ideas, and underlying concepts. The middle section focuses on what researchers have learned about cognition, that is, how people think and learn generally. The last section summarizes research on how people learn technological concepts and processes. Unfortunately, a great deal is still not known in this last area, a circumstance that is addressed in the committee’s recommendations in Chapter 8. Nevertheless, readers of the report, particularly those planning to design an assessment instrument for technological literacy, will want to familiarize themselves with this literature, because a clear idea of the cognitive processes involved in learning is crucial to the development of assessments and the interpretation of the results (NRC, 2001a):
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Tech Tally: Approaches to Assessing Technological Literacy [A] well-developed and empirically validated model of thinking and learning in an academic domain can be used to design and select assessment tasks that support the analysis of various kinds of student performance. Such a model can also serve as the basis for rubrics for evaluating and scoring pupils’ work, with discriminating features of expertise defining the specific targets of assessment. Testing and Measurement Basic Vocabulary Like any other field of knowledge, assessment has a specialized vocabulary. The terms “test” and “instrument,” for instance, which are often used interchangeably, refer to a set of items, questions, or tasks presented to individuals under controlled conditions. “Testing” is the administration of a test, and “measurement” is the process of assigning numbers, attributes, or characteristics—according to established rules—to determine the test taker’s level of performance on an instrument. The current emphasis on accountability in public schools, which entails accurate measurements of student performance, has renewed interest in measurement theory, which became a formal discipline in the 1930s. “Assessment,” derived from the French assidere (to sit beside), is defined as the process of collecting data to describe a level of functioning. Never an end in itself, an assessment provides information about what an individual knows or can do and a basis for decision making, for instance about a school curriculum. A related term, “evaluation,” implies a value judgment about the level of functioning. “Reliability” is a critical aspect of an assessment. An instrument is considered reliable if it provides consistent information over multiple administrations. For example, on a reliable test, a person’s score should be the same regardless of when the assessment was completed, when the responses were scored, or who scored the responses (Moskal and Leydens, 2000). Reliability is necessary, but not sufficient, to ensure that a test serves the purpose for which it was designed. Statistically, indices of test reliability typically range from zero to one, with reliabilities of 0.85 and above signifying test scores that are likely to be consistent from one test administration to the next and thus highly reliable (Linn and Gronlund, 2000). Assuming other aspects of an assessment remain
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Tech Tally: Approaches to Assessing Technological Literacy constant, reliability generally increases as the number of items or number of individuals participating increases. “Errors of measurement” can compromise the reliability of an assessment. Even if an instrument is carefully designed and found to be highly reliable, it can never be completely free of errors of measurement (OERL, 2006). This means a test taker’s true score is the sum of the observed score plus or minus measurement error. Errors can relate to the characteristics of the test taker (e.g., anxiety), the test administrator (e.g., inattention to proper test procedures), or the test environment (e.g., insufficient light or excessive noise), as well as to the accuracy of scoring. “Validity” refers to the soundness and appropriateness of the conclusions based on test scores. Validity answers questions such as “Is the test fair?”, “Does the test measure what it purports to measure?”, and “Are the test results useful for the intended purpose?” (Sireci, 2005). According to current measurement theory, a test or an assessment instrument in and of itself is not considered valid or invalid. Only the inferences based on the test results are valid or invalid. Various types of evidence may be used to determine validity, and all of them must relate to the underlying concept, or construct, being measured (AERA et al., 1999; Messick, 1989). Various types of evidence may be used to determine validity. One of the most important types of evidence for determining validity is how well the themes, wording, and format of test items relate to a specified target-content domain, which may be based on specific learning objectives, such as those spelled out in educational standards (e.g., ITEA’s Standards for Technological Literacy). A second type of evidence hinges on the relationship between test results and an external criterion, such as later success in college. A third type is based on a test taker’s response processes. For a test of technological decision making, for example, determining the content-specific problem-solving skills used by examinees to arrive at answers could provide important evidence of validity. When test scores are used or interpreted in more than one way or in different settings, each intended use or interpretation must be validated. In order to be valid, an assessment must be reliable, but reliability does not guarantee validity. That is, an instrument may produce highly stable results over multiple administrations but not accurately measure the desired knowledge or skill. Data from assessments should be reliable, and the inferences drawn from the data should be valid.
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Tech Tally: Approaches to Assessing Technological Literacy Central Themes In the course of this study, the committee returned again and again to several ideas of central importance to the development of high-quality assessment instruments. Although these themes are not the only important concepts in the field of assessment, they are given special emphasis in this report, which will be read by many people outside the field. The central themes are: (1) defining purpose; (2) selecting content; (3) avoiding bias; and (4) ensuring fairness. Defining Purpose Any assessment instrument can only assess a small part of what a person or group of people knows, believes, or can do. Thus, before starting the design process, it is important to define the purpose of the assessment. Although an assessment may serve more than one purpose, the most effective assessments are designed to serve only one purpose; different purposes all but imply different kinds of assessments. Completely different designs would be used, for instance, to test how well museum-goers understand the lessons of a technology exhibit and to determine how well graduates of a school of education have been prepared to teach technology to elementary school students. A designer must first establish what test takers will be expected to know about technology and what they should be able to demonstrate that they know. For students, these questions have often been answered in the form of standards. ITEA (2000) has developed content standards for K–12 students that address technological literacy. AAAS (1993) and NRC (1996) have developed national science education standards that include references to technological literacy. However, because none of these technology-related standards has been widely accepted or incorporated into education programs in the United States, the issue of assessment design can be very complicated. In the K–12 setting, researchers have identified a number of purposes for assessments. In the K–12 setting, researchers have identified a number of purposes for assessments, ranging from program evaluation and instructional planning to pupil diagnosis (e.g., Brandt, 1998; McTighe and Ferrara, 1996; Stiggins, 1995). Assessments of technological literacy have two primary purposes in the K–12 setting: (1) to provide a measure of what students and teachers know about technology and how well they are
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Tech Tally: Approaches to Assessing Technological Literacy able to apply it; and (2) to identify strengths and weaknesses in students’ understanding, so that changes in teaching and the curriculum can be made to address those weaknesses. For an assessment of technological literacy, the designer must ask what types of information the results will provide and to whom; how the results will be interpreted; and how useful the results will be. In contrast, the primary purpose of assessing the technological literacy of out-of-school adults should be to determine what the general populace knows and thinks about technology. At this point, little is known about the level of knowledge or practical skills of adults, and only slightly more is known about their attitudes toward technology. By contrast, a great deal is known about their political affiliations, television and movie viewing habits, health patterns, and buying trends. Assessments of technological literacy will provide information that can be used in a variety of ways, from designing museum exhibits to informing the design of new technologies. Selecting Content Because there are no explicit standards or expectations for what teachers and out-of-school adults should know or be able to do with respect to technology, assessment developers may wish to consider using a matrix like the one presented in Chapter 3, which is based in part on student standards, as a starting point for selecting appropriate content. Beyond the specific outcomes of learning, assessments must also take into account learning processes. Theories of cognitive learning based on a constructivist approach to knowledge acquisition suggest that the most valuable assessment instruments for students—at both the K–12 and post-secondary levels (i.e., pre-service teachers)—are integrated with instructional outcomes and curriculum content. Developers of assessments must have an understanding of instructional goals before they can design assessments to measure whether students have indeed met those goals. However, beyond the specific outcomes of learning, assessments must also take into account learning processes, that is, how students learn; this is an important gauge of what students can do once they leave the classroom. By integrating assessments with instruction, curriculum, and standards, assessments can not only provide valuable feedback about a student’s progress, but can also be used diagnostically to route students through instruction.
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Tech Tally: Approaches to Assessing Technological Literacy Avoiding Bias Assessment developers must be alert to the possibility of inequities in an assessment. An item is biased if it elicits different levels of performance by individuals with the same ability but from different ethnic, sexual, cultural, or religious groups (Hambleton and Rogers, 1995). Bias can be present in various forms. If one group uses a familiar term as slang for another concept, for example, the use of that word on an assessment might cause members of that group to give the wrong answer even if they understand the concept correctly. Pilot testing assessment items in small, sample populations is the best way to rule out bias. Suppose, for instance, that two questions seem identical, but the first has a correct response rate of 80 percent by all groups, and the second has an 80 percent correct response rate from all groups but one. Even if the bias is not apparent, the second question should not be used in the assessment. Another kind of bias may be present for low-income students who may lack experiences that other students take for granted (e.g., family vacations, travel, visits to movie theaters and restaurants, and exposure to a variety of toys and tools). These students may present novel difficulties for developers of assessments trying to measure their knowledge, skills, and understanding. Ensuring Fairness The issue of fairness is closely related to bias. If no photos, illustrations, or given names of people of a student’s ethnicity or race are included in a test, the student may not be motivated to do well on the test. If the only representation of a student’s background has a negative connotation, the student’s score may be adversely affected. Every effort should be made to avoid stereotypes and include positive examples of all groups (AERA et al., 1999; Nitko, 1996). Assessment developers must also take into account the extent to which those being assessed have had opportunities to acquire the knowledge or practice the skills that are the subject of the test. In the classroom setting, opportunities to learn may include access to instruction and instructional materials; time to review, practice, or apply a particular concept; teacher competence; and school environment and culture (Schwartz, 1995).
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Tech Tally: Approaches to Assessing Technological Literacy Ideally, test takers, whether they are students, teachers, or out-of-school adults, should be able to participate. For test takers with special needs, the test many have to be adjusted, either through accommodations, modifications, or, in rare instances, the use of alternative items or tasks. Adjustments may vary according to the particular case. For example, individuals with visual impairments require different modifications than individuals with dyslexia, although both may have trouble reading the text of a question. When making adjustments, test developers must ensure that the modified assessment measures the same knowledge or skills as the original assessment. Measurement Issues Each measurement method has advantages and disadvantages. Assessments can include many different types of questions and exercises, from true/false questions to the construction of a physical model that performs a certain function. Each measurement method has advantages and disadvantages, and test developers must select the ones that serve the purpose of the assessment. Additional measurement issues may arise depending on the amount of knowledge or number and types of skills an assessment attempts to capture. Selected-Response Formats Selected-response items present test takers with a selection of responses to choose from. Formats include true/false, multiple-choice, and matching questions. One advantage of selected-response items is that they generally require less response time by test takers and are easy to score. This does not mean they are easier to develop, however. Multiple-choice items, when developed to ensure validity and reliability, can not only probe for facts, dates, names, and isolated ideas, but can also provide an effective measure of higher-order thinking skills and problem-solving abilities. Indeed, well constructed multiple-choice items can measure virtually any level of cognitive functioning. One weakness of the selected-response format is that test takers can sometimes arrive at correct answers indirectly by eliminating incorrect choices, rather than directly by applying the knowledge intended by the test developer. In such cases, an assessment is measuring test-taking skill rather than knowledge or capability.
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Tech Tally: Approaches to Assessing Technological Literacy Constructed-Response Formats In constructed-response questions, such as short-answer questions or essay questions, the test taker must provide a response. In general, constructed-response items provide a more in-depth assessment of a person’s knowledge and ability to apply that knowledge than selected-response items. That advantage is counterbalanced, however, by the disadvantage that constructed-response questions are more difficult, time consuming, and subjective to score (Luckhel et al., 1994). Performance-Assessment Formats Performance assessments include exhibits, hands-on experiments, and other performance tasks, such as the construction of a device out of given materials that meets specified requirements. One advantage of performance assessments is that they can measure the capability—or “doing”—dimension of technological literacy. A disadvantage is that they are generally more time-consuming and expensive to develop and to administer than other types of assessments items. In addition, if the use of one or more performance tasks significantly reduces the total number of items in an assessment, the overall reliability of the assessment may be adversely affected (Custer et al., 2000). Effective, Practical Formats Many effective assessments, including some large-scale, statewide tests, combine at least two formats. In assessing technological literacy, multiple-choice and short-answer questions might be used to measure facts, knowledge, and concepts related to technological literacy, as well as the types of knowledge that can be applied in different situations. However, depending on the objective of the assessment, the latter skill might also be measured by performance tasks. Real or simulated performance tasks may be the best way for determining how well an individual can apply knowledge and concepts to solving a particular problem. Domain of Knowledge Often educators or researchers are interested in finding out what people know and can do related to a wide-ranging domain of knowledge.
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Tech Tally: Approaches to Assessing Technological Literacy Because the time and costs of testing would be extensive, it is usually not feasible to develop a single test to measure a very large body of knowledge. Assessment experts have devised a solution to this dilemma—giving only a fraction of the total number of items to each test subject. Dividing a large test into smaller segments and administering each segment to a portion of the population of interest is called “matrix sampling.” The results are reliable at the level of the total population tested as well as for certain subgroups (e.g., by gender or age) but not at the level of the individual, and individual results are not reported. The National Assessment of Educational Progress and the Trends in International Mathematics and Science Study use matrix-sampling techniques. So-called census testing involves giving the same test to all members of the target population. Because testing time is generally limited, an entire domain of knowledge cannot be assessed in this way. The advantage of census testing is that the results are reliable and can be reported at the level of the individual. State grade-level assessments are examples of testing by the census approach. Reporting of Results The way the results of an assessment are reported depends on the purpose of the assessment and the methods used in its development. The most common presentation of results is basic and descriptive—for example, the percentage of individuals who correctly respond to an item or perform a task. Other types of reporting methods include: norm-referenced interpretation; criterion-referenced interpretation; and standards-based interpretation. Norm-Referenced Interpretations Norm-referenced results are relative interpretations based on an individual’s position with respect to a group, often called a normative sample. For example, a student might score in the 63rd percentile, which means that he or she scored better than 63 percent of the other students who took the test or, perhaps, better than 63 percent of a previous group of students who are the reference group (the norm) against which the test was standardized. Because norm-referenced results are relative, by definition some individuals score poorly, some average, and some well.
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Tech Tally: Approaches to Assessing Technological Literacy Criterion-Referenced Interpretations Criterion-referenced interpretations are presented in absolute rather than relative terms and indicate how well individuals perform absolutely, not on how well they perform relative to others. The criterion is a desired learning outcome, often based on educational standards, and assessment items measure how well the test taker demonstrates knowledge or skill related to that goal. Criterion-referenced results may be presented as a number on a scale, a grade, or a rubric (e.g., novice, adequate, proficient). Thus, depending on the assessment and the group being assessed, few, half, or a large number of individuals (or groups) could meet the established criteria. Standards-Based Interpretation Standards-based interpretation is closely related to criterion-based interpretation. The No Child Left Behind Act of 2001 requires that each state develop an assessment program based on a standards-based interpretation of results, which ultimately allows for 100 percent of students, overall and disaggregated by subgroup, to be 100 percent proficient in reading, mathematics, and starting in 2007, in science. To define proficiency, each state education agency was required to submit a workbook plan to the U.S. Department of Education for approval based on accepted standards-setting techniques, such as Bookmark or Modified Angoff (Kiplinger, 1997). Standards-based interpretation, like criterion-based interpretation, has a proficiency-defining “cut-off” score. Cognition In the assessment triangle described in Knowing What Students Know (NRC, 2001b), one corner of the triangle is cognition. In the context of the present report, cognition is a theory or set of beliefs about how people represent knowledge and develop competence in a subject domain. To test an individual’s learning and knowledge, assessment designers must first understand how people learn and know things. An explicit, well conceived cognitive model of learning is the basis of any sound assessment design; the model should reflect the most scientifically credible evidence about how learners represent knowledge and develop expertise.
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Tech Tally: Approaches to Assessing Technological Literacy Most experienced teachers have an understanding of how their students learn, although that understanding may not be scientifically formulated. As researchers learn more about how people learn and understand, the new understanding should be incorporated into assessments. An assessment should not be static but should be constantly evolving to reflect the latest and best research. Nature of Expertise Many principles about thinking and learning are derived from studies of the nature of expertise and how it is developed. Experts have a great deal of both declarative (knowing that) and procedural (knowing how) knowledge that is highly organized and can be efficiently retrieved to solve problems. Thus, cognitive scientists have focused considerable efforts on studying expert performance in the hope of gaining insights into thinking, learning, and problem solving. These studies reveal marked differences between experts and novices (defined as individuals in the early stages of acquiring expertise). To become an expert, a person must have many years of experience and practice in a given domain. During those years, the individual collects and stores in memory huge amounts of knowledge, facts, and information about his or her domain of expertise. For this knowledge to be useful, however, it must be organized in ways that are efficient for recall and application (Bransford et al., 1999; Chi and Glaser, 1981; Ericsson and Kintsch, 1995). Researchers have found that expert knowledge is organized hierarchically; fundamental principles and concepts are located on the higher levels of the hierarchy and are interconnected with ancillary concepts and related facts on the lower levels of the hierarchy. In addition, procedures and contexts for applying knowledge are bundled with the knowledge so that experts can retrieve knowledge in “chunks” with relatively little cognitive effort. This so-called “conditionalized knowledge” makes it possible for experts to perform high-level cognitive tasks rapidly (Anderson, 1990). Researchers have found that expert knowledge is organized hierarchically. Thanks to this highly organized store of knowledge, experts can focus their short-term memory on analyzing and solving problems, rather than on searching long-term memory for relevant knowledge and procedures. In addition, experts can integrate new knowledge into their existing knowledge framework with relatively little effort. For an expert, “knowing more” means having (1) more conceptual chunks of knowledge in memory,
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Tech Tally: Approaches to Assessing Technological Literacy Researchers have also studied the development of visualization and spatial skills in adolescents and younger teens, who are capable of working with simple symbolic, mathematical models, but who respond most readily to computer and concrete, three-dimensional (3-D) models. Although these children tend to have complex imaginations, unless they have sketching and drawing skills, they have difficulty representing the designs in their mind’s eye in two-dimensional space. For example, Welch et al. (2000) found that 12- to-13-year-old novice designers approach sketching differently than professional designers, who use sketching to explore ideas and solutions. Although adolescents and teens may not be adept at sketching and drawing, they tend to develop design ideas by working with 3-D models. These and other observations by researchers raise questions about the differences between school-based design and professional design (Hill and Anning, 2001a,b). Novices and experts approach technological design tasks differently, just as they do in other domains of learning. Both novice and expert designers manage a range of concurrent cognitive actions, but novices lack metacognitive strategies for organizing their activities (Kavakli and Gero, 2002). In a study of how expert, novice, and naïve designers approach the redesign of simple mechanical devices, Crismond (2001) found that all three groups relied more heavily on analytic strategies than on evaluation or synthesis. Not surprisingly, expert designers were able to generate more redesign ideas than designers with less experience. Novices and experts approach technological design tasks differently. Research on the development of expertise has also focused on the relationship of procedural to conceptual knowledge, both of which appear to be necessary for successful design, for novices as well as experts. In addition, the content of the procedural knowledge is determined by the design problem to be solved. In other words, different design problems require different approaches. The connection between procedural and conceptual knowledge in educational settings was investigated by Pomares-Brandt (2003) in a study of students’ skills in retrieving information from the Internet. In this study, a lack of conceptual knowledge of what the Internet is and how it functions had a negative impact on information-retrieval skills. Critical thinking and decision making in children, as in adults, suggest the level of reasoning necessary to making sensible choices regarding technological issues. Taking advantage of the relative comfort in distance afforded by virtual reality, researchers have used digital simulations to prompt students to reason through a variety of moral dilemmas
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Tech Tally: Approaches to Assessing Technological Literacy (e.g., Bers, 2001; Wegerif, 2004). Researchers in Germany found that when ethics was taught in school it was often perceived to be “just another school subject” or misunderstood to be religious instruction (Schallies et al., 2002). About 60 percent of the more than 3,000 high school students surveyed in this study did not think they had been prepared in school to deal with the types of ethical decisions that commonly face practitioners in science and technology. Researchers are also beginning to explore the roles students negotiate in relation to technology (Jenson et al., 2003; Selwyn, 2001, 2003; Upitis, 1998; Zeidler, 2003). Students’ identities are increasingly defined through these roles in terms of competence, interests, and status. Ownership of cell phones or MP3 players, for example, confers status in a culture in which students are heavily influenced by media pressure from one direction and peer pressure from another. Ethical decision making by adults may be commonplace, but research suggests it is difficult to specify how ethical decisions are made (Petrina, 2003). Research on software piracy reveals that moral reasoning on technological issues has contingencies. For instance, university students typically recognize unethical behavior, but make decisions relative to their desires. Nearly three-quarters of 433 students in one study acknowledged participating in software piracy, and half of these said they did not feel guilty about doing so (Hinduja, 2003). Learning Related to Engineering2 Historically, research on engineering education has mostly been done by engineering faculty and has focused on changing curricula, classrooms, and content rather than on measuring the impact of these changes on what students know and can do. Recently, however, as academic engineering faculty increasingly collaborate with faculty in other disciplines, such as education, psychology, and sociology, the types of research questions being asked and the assumptions being made are beginning to change. Because of the shift toward investigating how people learn engineering, most of the available research is based on qualitative methodologies, such as verbal protocol analysis and open-ended questionnaires. Research on how individuals learn the engineering design process 2 This section includes material adapted from Waller, 2004.
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Tech Tally: Approaches to Assessing Technological Literacy is focused mostly on comparing novice and experienced designers. These studies indicate that novices use a trial-and-error approach, consider fewer issues when describing a problem, ask for fewer kinds of information, use fewer types of design activities, make fewer transitions between design activities, and produce designs of lower quality than experienced designers (e.g., Adams et al., 2003; Ahmed et al., 2003; Atman et al., 1999; Mullins et al., 1999). Other findings are also relevant to assessing design activity: (1) the choice of task affects problem-solving behavior; (2) more evaluation occurs in the solving of complex problems than simple problems; (3) students draw on personal experiences with the problem situation to generate solutions; and (4) sketching not only allows the problem solver to store information externally, but also allows him or her to experiment with reality, iterate the solution space, and reason at the conceptual and systems level. Assessing mental models can be very tricky because questions about different, but parallel, situations evoke different explanations. In addition, people who have more than one model for a concept (e.g., electricity as flow and electricity as a field phenomenon) may use the simpler model to explain a situation unless they are asked specifically for the most technically precise explanation. Since the early 1980s, researchers have been trying to capture the mental models children, students, and adults use to understand concepts and processes, such as combustion, electricity, and evaporation (e.g., Borges and Gilbert, 1999; Tytler, 2000; Watson et al., 1997). However, because the vast majority of studies on conceptual change involve single or comparative designs, rather than longitudinal designs, the conclusions require assumptions of equivalence of samples and populations. Assessing mental models can be very tricky. Taken together, these studies indicate several features of mental models: (1) they are developed initially through everyday experiences; (2) they are generally simple, causal models of observable phenomena; and (3) they are applied consistently according to the individual’s rules of logic (which may not match those accepted in the scientific community). In addition, individuals can hold alternative conceptions simultaneously without apparent conflict. Thus, different questions may elicit different models from the same individual. One way of measuring students’ conceptual understanding, rather than their ability to apply formulae, is through a concept inventory. First
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Tech Tally: Approaches to Assessing Technological Literacy developed in physics education in the Force Concept Inventory (Hestenes et al., 1992), concept inventories consist of multiple-choice questions that require a substantial understanding of concepts rather than simple calculation skills or commonsense understanding. By including a variety of distractors, such assessments reveal the extent and nature of student misconceptions about a topic. In engineering education, 15 concept inventories are in various stages of development (Box 4-1). Thus far, no studies have addressed general engineering concepts, such as systems, boundaries, constraints, trade-offs, goal setting, estimation, and safety. Some of these are obliquely included in analyses of design behavior, but no study addresses how participants specifically include these concepts. In addition, not a single study investigates what the general public understands about these concepts, much less how they come to understand them. When applying the findings of studies of how people learn engineering design and content, several caveats must be observed. First, engineering students and practitioners are not a random sample of the general population; therefore findings based on this specialized population may not apply to other populations. Second, learning preferences not only affect the way people learn, but also how they interact with assessment instruments, and engineering concepts can be expressed in many different ways (e.g., mathematics, diagrams, analogies, and verbal descriptions). Thus, a robust assessment instrument should accept several different expressions of concepts as “correct.” Third, engineering design is ultimately a collaborative process, with goals, boundaries, constraints, and criteria negotiated by a wide variety of stakeholders. Therefore, an authentic assessment of design skills should include a component that reflects the BOX 4-1 Concept Inventories Under Development, by Topic Electronics Waves Thermodynamics Strength of materials Signals and systems Electromagnetics Circuits Fluid mechanics Materials Chemistry Dynamics Thermal and transport processes Computer engineering Statistics Heat transfer Source: Waller, 2004.
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Tech Tally: Approaches to Assessing Technological Literacy negotiation, teamwork, and communication skills necessary for successful design processes. Fourth, because design is context sensitive, researchers must be cautious in comparing results across cultures (including cultures within the United States) (Herbeaux and Bannerot, 2003). The values underlying choices and trade-offs between groups may be very different, as may the communication and negotiation processes. And, because understanding depends in part on everyday experiences, assessors must be careful to select examples and situations that do not reflect socioeconomic or cultural differences. For example, some children may not have experience with clothes drying on a line, while others may never have seen a light wired to a dimmer switch. If these items are used, an assessment instrument may indicate differences in conceptual understanding that actually reflect socioeconomic and/or cultural differences among study participants. References AAAS (American Association for the Advancement of Science). 1993. Benchmarks for Science Literacy. Project 2061. New York: Oxford University Press. Adams, R.S., J. Turns, and C.J. Atman. 2003. Educating effective engineering designers: the role of reflective practice. Design Studies 24(2003): 275–294. AERA (American Educational Research Association), APA (American Psychological Association), and NCME (National Council on Measurement in Education). 1999. Fairness in Testing and Test Use: Standards 7.3 and 7.4. Pp. 79–82 in Standards for Educational and Psychological Testing. Washington, D.C.: AERA. Ahmed, S., K.M. Wallace, and L.T.M. Blessing. 2003. Understanding the differences between how novice and experienced designers approach design tasks. Research in Engineering Design 14(2003): 1–11. Anderson, J.R. 1990. Cognitive Psychology and Its Implications. San Francisco: Freeman. Atman, C.J., J.R. Chimka, K.M. Bursic, and H. Nachtmann. 1999. A comparison of freshman and senior engineering design processes. Design Studies 20(2): 131–152. Barnett, S.M., and S.J. Ceci. 2002. When and where do we apply what we learn?: a taxonomy for far transfer. Psychological Bulletin 128(4): 612–637. Bassok, M., and K.J. Holyoak. 1989. Interdomain transfer between isomorphic topics in algebra and physics. Journal of Experimental Psychology: Learning, Memory, and Cognition 15(1): 153–166. Bers, M.U. 2001. Identity construction environments: developing personal and moral values through design of a virtual city. Journal of the Learning Sciences 10(4): 365–415. Bjork, R.A., and A. Richardson-Klavhen. 1989. On the Puzzling Relationship Between Environment Context and Human Memory. Pp. 313–344 in Current Issues in Cognitive Processes: The Tulane Flowerree Symposium on Cognition, edited by C. Izawa. Hillsdale, N.J.: Lawrence Erlbaum Associates.
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