ARTICLE pubs.acs.org/jchemeduc
Decay of Student Knowledge in Chemistry Diane M. Bunce,*,† Jessica R. VandenPlas,‡,|| and Cameron Soulis§ †
Department of Chemistry, Catholic University of America, Washington, D.C. 20064, United States Department of Chemistry, Grand Valley State University, Allendale, Michigan 49401, United States § Science Department, Sidwell Friends School, Washington, D.C. 20016, United States ‡
ABSTRACT: It is a common complaint among teachers that students forget what they have learned soon after taking a test. The phenomenon is seen in both secondary and undergraduate chemistry courses. This study examines the length of time after a test that students in three different chemistry courses (undergraduate nursing, nonscience majors, and high school honors) are able to successfully answer the same free response achievement questions that were on the test. Overall effects on achievement were found to be nonsignificant, as were interaction effects of time interval and logical reasoning level. However, a differential effect between courses on achievement was found, leading to the conclusion that students enrolled in courses in which the continued spiral use of chemistry concepts is not evident, frequent quizzing opportunities are not provided, and a final exam is not given experience a significant decrease in achievement during the first 48 h following a test. This decrease remains constant for at least 2 weeks. Students enrolled in courses in which the spiral use of chemistry concepts is more explicit and regular quizzing opportunities and comprehensive final exams are given did not show a significant decrease in achievement from the original testing to delayed quizzing occasions over a 17-day period. KEYWORDS: First-Year Undergraduate/General, High School/Introductory Chemistry, Chemical Education Research, Testing/ Assessment, Learning Theories FEATURE: Chemical Education Research
’ THEORY Students enrolled in college general chemistry and high school chemistry courses are evaluated primarily by their achievement on tests. Teachers often report that students quickly forget information that they correctly answered on tests given just days before. If this is true, then remediation efforts are warranted. If it is not true, then our curricula or other factors (both conceptual and social) may be at work in ways that promote student success. The purpose of this research is to separate fact from fiction. The first step in understanding knowledge decay is to document whether the decay actually exists and if it does, when it occurs. To this end, a classroom experiment (versus an educational laboratory experiment) using real classes was devised that looked at the decay of general chemistry knowledge among three distinct populations—undergraduate nursing majors, undergraduate nonscience majors, and high school honors students. Wheeler et al.1 report that undergraduates experience extinction of learning (decay of knowledge) within 48 h of a testing situation. However, if multiple tests are administered after the initial test, students will regain some of the knowledge originally lost. Rickard2 hypothesizes that the additional practice retrieving information during these multiple testing situations may be responsible for the partial remediation of the situation. Karpicke and Roediger3 concur that repeated testing after learning produces a positive effect on delayed recall of knowledge. Much of this research is based on Anderson’s theory of memory,4 which provides a conceptual basis for this research by explaining that multiple retrievals of the same information result in the increased likelihood of correct retrievals in future situations. If this is Copyright r 2011 American Chemical Society and Division of Chemical Education, Inc.
true, then multiple testing of concepts could be used to help students develop a stronger or deeper understanding of concepts resulting in increased long-term retention. Another piece of the puzzle of knowledge decay is how the information is initially presented. Hockley5 reports that discrete item information is forgotten more quickly than associative information. Discrete item information is factual information that is presented in isolation from other concepts. An example might be that acids release hydrogen ions. Conversely, associative information relates new information to that previously learned or links two or more pieces of information together when the information is presented. An example of associative learning would be that acids react by producing hydrogen (or hydronium) ions that are attracted to negative ions or negatively charged areas in a polar molecule. Here, the concepts of “what is an acid” and the underlying chemical reactivity are linked or associated. This too can be related to Anderson’s theory of memory4 in that associative information has more retrieval paths than discrete item information and is therefore more likely to be successfully recalled in future testing occasions. The distinction between discrete item and associative information can be more clearly understood in terms of two curricula models that are currently used in chemistry teaching today. Some curricula emphasize complete presentation of a concept in a chapter and then move on to other concepts. This linear or modular approach has been used in science textbooks for many
Published: July 22, 2011 1231
dx.doi.org/10.1021/ed100683h | J. Chem. Educ. 2011, 88, 1231–1237
Journal of Chemical Education
ARTICLE
Table 1. Differences in Course Structure among the Three General Chemistry Courses Course Features Course
Tests per Semester
Intervening Quizzes
Homework Collected and Graded
Cumulative Final Exam
Number of Tests in This Study
Nursing (I and II)
4
Daily
No
Yes
3
Nonscience Majors
4
None
Yes
No
3
High School Honors
3
2 3 between tests
Yes
Yes
2
years. In some new curricula,6,7 students are presented with information on a need-to-know basis but concepts are revisited several times during a chapter or over several chapters, providing more detail and links to prior learning each time. This is sometimes referred to as a spiral or associative curriculum. Spiral curricula can still be viewed as linear by students if they do not see the explicit connection between one presentation of a specific topic and the next. There may be many reasons why teachers report that students forget information that they had successfully demonstrated on recent tests. This study will suggest some plausible reasons for the knowledge decline; however, this study dealt primarily with whether such a decline actually exists. The reasons for a knowledge decay phenomenon may include how the information was taught (discrete item vs associative) and how often students are asked to recall the information (on tests or in other situations). Other equally valid reasons may include the magnitude of the gap between student aptitude and the cognitive demands of the material being presented; the level of student motivation to succeed in the course; and the internal framework of the curricula, including what and how often engagement is required of students. The combination of questions concerning if and when the decay of knowledge takes place and the reasons for the decay is too large to be adequately addressed in a single study with a limited population. Such research would require several studies each contributing a small part of the answer. The purpose of this research is to examine whether the phenomenon of knowledge decay in chemistry actually takes place and, if so, how soon after a chemistry test students experience such knowledge decay. This research also looked at the stability of the knowledge decay. In other words, if a knowledge decay is detected, is the decay stable over a period of up to two weeks following a test? It was decided to investigate this phenomenon with several different identifiable populations with different chemistry curricula approaches. This study included college-level nursing students enrolled in a general, organic, and biochemistry course; nonscience majors enrolled in a nonscience majors’ course that emphasized chemistry as applied to both global and personal issues; and a high school honors chemistry course.
’ RESEARCH QUESTIONS The research questions that formed the basis of this study were as follows: 1. Do chemistry students in a variety of general chemistry courses experience decay in their knowledge following regularly scheduled testing occasions? 2. If detected, how is this decay affected by the variables of length of time following the testing occasion, type of course, and student logical reasoning level?
’ METHODOLOGY Sample
Undergraduates at a medium-sized university and high school honors students at a private high school in the mid-Atlantic region served as subjects in this experiment. The undergraduates included 73 nursing majors in two concurrently taught semesters of a yearlong general, organic, and biochemistry course (n = 44 for nursing chemistry I and n = 29 for nursing chemistry II), and 44 undergraduate nonscience majors enrolled in a Chemistry in Context course. The average class level of the nursing students was that of first-year students while the average class level for nonscience majors was second-year. The high school students (n = 38) were enrolled in one of three sections of the same honors course taught by the same teacher; these students had an average class level of 10th grade. For each of these courses, whole class data were analyzed following Institutional Review Board guidelines and approvals.
’ VARIABLES Differences in Course Structure
Several differences existed in the course structure of these three courses in terms of final exams, intervening quizzes (online or paper and pencil), and collection and grading of homework assignments on a regular basis. These differences are summarized in Table 1. The information in Table 1 shows that all three courses offered some type of repeated grading occasions for chemistry concepts between tests, whether it was quizzes or graded homework. Two of the three (nursing and high school honors) had cumulative final exams. Pretest
All students completed either a paper or online version of the Group Assessment of Logical Thinking (GALT) Test.8 The GALT test is a 12-question Piagetian test. For the first 10 questions, students must choose both a correct answer and a correct reason in order for the question to receive a score of correct. The last 2 questions are combination-of-variable questions for which students must list all possible combinations of the variables provided. The GALT test has been shown to positively correlate with achievement in chemistry.9 In this study, students were directed to take the test either online, if available, or on paper. The average time of this test is between 10 and 12 min. The instructors explained that the results of the GALT test would not affect students’ grades in the course but would be used to help the teacher gear the teaching level of the course to students’ logical reasoning ability level. Upon the basis of the time stamp for those who took the GALT test online, it can be assumed that students took the test seriously and did not race through its completion without taking time to read each question. Students were generally eager to provide information to the instructor that would affect the level at which the course was taught. 1232
dx.doi.org/10.1021/ed100683h |J. Chem. Educ. 2011, 88, 1231–1237
Journal of Chemical Education
ARTICLE
Table 2. Course and Corresponding Conceptual Sample Questions Course
Question
Undergraduate
The density of water at 37 °C is 0.993 g/mL (the density changes as temperature increases). The density
Nursing, Part I
of normal urine ranges from 1.003 to 1.030 g/mL at the same temperature. Explain why the density of urine is greater than the density of water and how this can be used to diagnose some illnesses.
Undergraduate
Blue-colored solutions transmit blue light and absorb red light. Are blue solutions absorbing light at a
Nonscience Majors
higher or lower energy than they are transmitting? Explain your answer.
High School Honors
Even though the oxygen demands of trout and bass are different, they can exist in the same body of water. However, if the temperature of the water in the summer gets above 23 °C, the trout begin to die,
Chemistry
but not the bass. Why is this the case?
Table 3. Testing Schedule for Experimental Cohorts Test
Time 1 (2 5 days)
Time 2 (6 9 days)
Time 3 (10 17 days)
1
Cohort 1
Cohort 2
Cohort 3
2 3
Cohort 3 Cohort 2
Cohort 1 Cohort 3
Cohort 2 Cohort 1
The range of GALT scores within courses enabled the researchers to assign students to one of three relative GALT achievement groups (high, middle, or low) to test the effect of logical reasoning ability on the decay of student knowledge. The divisions between GALT groups were slightly different for each course and were chosen to provide equivalent number of students in each of the high, middle, and low groups per course. GALT groups were not compared between courses in the analysis; therefore, a slightly different cutoff for a high GALT group in the nonscience major’s course compared to the cutoff for the high GALT group in the high school honors course would not adversely affect the analysis. Achievement
Achievement was measured with open-ended, course-specific, conceptual and skill questions included on regularly scheduled full period exams. A subset of these questions was repeated on paper or online quizzes following the exam at predetermined time intervals. Data from three of the four hour-long tests in the courses, followed by a series of quizzes, were used for the undergraduate students in this study. Data from two of the three hour-long tests, followed by a series of quizzes, were used for the high school students. Sample conceptual questions are included in Table 2. Student achievement on both tests and quizzes that repeated a subset of the test questions within a given course was assessed by the same grader using a rubric designed by the research team. The same grading rubric was used for both test and quizzes and investigated the presence of chemistry concept understanding.
’ EXPERIMENTAL DESIGN In each chemistry course (undergraduate nursing, undergraduate nonscience major, and high school honors), tests were scheduled at approximately one-month intervals. Following each test, a series of three quizzes repeating a subset of test questions was administered at different time intervals. The three time intervals were as follows: Time 1 (2 5 days following the test), Time 2 (6 9 days), and Time 3 (10 17 days). Students in each course were assigned to different experimental cohorts that completed only one quiz following each test
Table 4. GALT Scores for All Courses n
Mean
Std. Deviation
Undergraduate Nursing, Part I
44
6.89
1.99
Undergraduate Nursing, Part II Undergraduate Nonscience Majors
29 44
6.66 7.45
2.54 2.52
High School Honors
38
10.18
1.41
Course
at a specific time interval. This technique was used to ensure that a specific student would not always complete a quiz at the same time interval. Students were assigned to these cohorts using a stratified random sampling technique based upon their GALT grouping. This resulted in each experimental cohort containing students of differing GALT levels. The students remained part of the same experimental cohort for data collection purposes only for the entire experiment. The experimental cohorts were rotated among the time intervals following each of the tests used in this study, as described in Table 3. This rotation resulted in a specific cohort completing a quiz at one time interval for Test 1 and a different time interval for Tests 2 and 3. Students were not necessarily aware of being placed in a particular experimental cohort. This categorization was used only by the researchers. In the two undergraduate courses (nursing and nonscience majors), students received e-mails reminding them when they should take the online quiz. Students were directed to take these quizzes without use of notes, books, or other people. Time for each student’s online quiz was recorded by the computer and checked by the researchers to ensure that students were not taking an inordinate amount of time to complete the quiz. Students were awarded extra points for completing the online quizzes. These points were awarded when the quiz was completed regardless of the score attained on the quiz. High school honors students were given paper quizzes on the appropriate days for each cohort either at the beginning of class or during lab.
’ RESULTS Screening of Data by GALT Score
A one-way analysis of variance (ANOVA) with GALT score as the dependent variable and course as the independent variable was performed to determine whether a difference existed in the average GALT score among the courses. Before analyzing the results of this test, one must test a prerequisite for ANOVA, which is the assumption of homogeneity of variance of the data. This assumption is checked by the Levene test. The data in this study violate the homogeneity of variance assumption (F3, 151 = 5.32, 1233
dx.doi.org/10.1021/ed100683h |J. Chem. Educ. 2011, 88, 1231–1237
Journal of Chemical Education
ARTICLE
Table 5. Course Test and Quiz Descriptive Statistics Course Type Nursing Nonscience Majors High School Honors
Assessment Type
Mean Score, %
n for Questions
Std. Deviation
Std. Error Mean
Test Question
66.73
416
32.78
1.61
Quiz Question
68.80
416
31.22
1.53
Test Question
78.15
230
31.42
2.07
Quiz Question
64.99
230
30.96
2.04
Test Question
80.42
148
27.43
2.25
Quiz Question
78.10
148
32.16
2.64
p = 0.002). The literature suggests, however, that ANOVA is relatively robust in relation to this violation, and that setting a more stringent level of significance can address such a violation and work as an added precaution against Type 2 error.10 For the results of this ANOVA, a more stringent level of significance (p < 0.01) was adopted as suggested by the literature10 as opposed to the more common significance level of p < 0.05. Table 4 gives the means and standard deviations of the GALT scores for all courses involved in this study. Even at the more conservative level of significance, there is a significant overall effect for GALT and course in this study (F3, 151 = 21.294, p = 0.000). A Tukey post-hoc analysis shows that there is a significant difference only between the overall GALT scores of the high school honors course and all other courses (p = 0.000). The high school honors course demonstrated a significantly higher GALT mean than any of the other undergraduate courses. There is no significant difference in the mean of GALT scores for any of the college level courses (nursing part I, part II, or nonscience majors). Because the GALT scores for the undergraduate nursing part I and part II courses were not significantly different, these two parts of the yearlong nursing chemistry course were combined for purposes of analysis. The reasoning behind this decision, besides the nonsignificant GALT scores, was that these two courses also used the same textbook, had the same teacher, with the same teaching approach, curricula, and curriculum framework. The data from these two courses were combined into a single “undergraduate nursing” variable for the purpose of this statistical analysis. Analysis of Test and Quiz Question Achievement Scores
The quiz and test question achievement results from all three chemistry courses (undergraduate nursing, undergraduate nonscience major, and high school honors) were analyzed separately using three different mixed between within subjects ANOVAs because the textbook, curricula, curriculum framework, teaching practices, and questions on tests and quizzes used in each course differed substantially. This was especially true between the undergraduate and high school honors courses. For each course, the three regularly scheduled tests (two for high school students) were loaded into a single “test question” variable. The questions used on quizzes regardless of time delay were loaded into a separate “quiz question” variable for each course. Combining all test scores into a single variable and all quiz scores into a separate variable meant that the content of each individual question could not be analyzed. A larger sample would be needed to test the effect of content on knowledge decay. We used the most robust statistical analysis for the data we collected. For example, students took each quiz only once in an attempt to prevent test familiarization that could result in inflated scores and test fatigue. For this reason, a direct comparison cannot be made for an
individual student on a single question at quiz time intervals 1, 2, and 3. To compare the interaction effect of the quiz time intervals, students of each time cohort were compared to one another. For instance, for a single question, students at time interval 1 were compared to students at intervals 2 and 3. An individual student’s performance was not compared across all three intervals. This type of analysis resulted in the variable “quiz delay” becoming a between subjects variable, rather than a within subjects variable. For each of the three mixed between within subjects ANOVA (one per course), the between-subject factors were quiz time interval and GALT group. The within-subject factor was the repeated measures comparison between the dependent variables of test and quiz question achievement. Table 5 provides the achievement means and standard deviations across all test and quiz questions for each course. The sample sizes for the calculation of the means and standard deviations reported in Table 5 are based upon the number of questions that each student answered, and not the sample sizes of each course. For example, a nursing student would have answered two questions on a test for each of three tests, for a total of six questions. Therefore, the maximum n = 79 students 6 questions = 474 questions answered by nursing students. This n differs from the n reported in Table 5 (n = 416) because only complete sets of data were entered into the analysis. Thus, the results of any student who did not complete a quiz after each of the three exams were not included in the analysis. High school students completed two questions on each of two exams yielding n = 38 students 4 questions = 152 questions answered by high school honors students minus incomplete data sets. The data were combined in this manner because we were interested in the overall decay of knowledge across all three variables of concepts tested, tests, and students. An examination of the means between test and quiz variables shows little change for the nursing and high school honors class (nursing means = 66.73 [test] and 68.70 [quiz]; high school honors means = 80.42 [test] and 78.10 [quiz]). A noticeable difference was found in means for the nonscience majors between the test and quiz (nonscience major means = 78.15 [test] and 64.99 [quiz]), but the significance of these comparisons must be checked by ANOVA for each course before a conclusive result can be reported. Assumptions
The assumption of homogeneity of variance for each the three ANOVAs was tested as discussed above, using the Levene test for equality of variances. The results are as follows: the Levene’s test is nonsignificant (p > 0.05) as required for the ANOVA in both nursing and high school courses. In the nonscience majors’ course, for which the Levene test shows violation of this assumption (p = 0.000 for both test and quiz question scores), the issue of nonequality of error variances is again controlled for 1234
dx.doi.org/10.1021/ed100683h |J. Chem. Educ. 2011, 88, 1231–1237
Journal of Chemical Education by selecting a more stringent level of significance for the ANOVA results. A more conservative probability of 0.01 is therefore used as a test of significance in this study as a means of controlling for type II error,10 as opposed to the more common significance level of 0.05. Overall Test Achievement Effects
For the nursing general chemistry course, the overall main effect for test versus quiz achievement was not significant (F1, 407 = 2.01, p = 0.16). This indicates that students in this course showed no significant difference in their achievement scores on the matched test and quiz scores over time. In the nonscience majors’ course, there was a significant overall main effect for test versus quiz achievement across the three testing occasions (F1, 221 = 26.77, p = 0.000). This level of significance is below the conservative level of 0.01 that was chosen for this study. The significant difference exhibits a medium level of statistical power (partial η2 = 0.108).10 We interpreted this as there being a significant difference in achievement for the nonscience majors on the matched test and quiz scores over time. The high school honors course did not show a significant overall main effect for test versus quiz achievement (F1, 113 = 0.01, p = 0.92) across the two test occasions. Two-Way Interaction Effect between Time Interval and Achievement
Because the nonscience majors’ course was the only one that showed a significant difference between test versus quiz achievement, this was the only course in which the two-way interaction effect for time and achievement could be investigated. The twoway interaction effect between quiz time interval and test versus quiz achievement was not significant (F2, 221 = 1.92, p = 0.15). This indicates that there was no significant difference in achievement between the first time interval (2 5 days) and either of the other two time intervals (6 9 and 10 17 days). Students performed similarly on quiz questions across all three time intervals. Because there is an overall significant difference for the nonscience majors’ achievement scores between the test and delayed quizzes, the drop in achievement must have taken place after the test but prior to the first measured time delayed quiz (2 5 days). This is interpreted as the decay of learning occurring within the first 2 days (48 h) following the test. Main and Interaction Effects of GALT on Test and Quiz Achievement
The main effect for GALT was significant for both the nonscience majors (F2, 221 = 5.76, p = 0.004) and nursing (F2, 407 = 4.89, p = 0.008) courses. This shows us that high, middle, and low GALT students in these courses perform differently on the dependent variables of test and quiz achievement. This has been shown in the literature,9 with students of high logical reasoning ability outscoring students of low logical reasoning ability in general chemistry. For the high school course, there was no significant main effect for GALT score (F2, 113 = 0.102, p = 0.750). This may be due to the homogeneity of the logical reasoning ability of the high school population, showing little overall variation in GALT score. To address the research questions of this study, we were most interested in the two-way interaction effect between GALT group (high, medium, low) and test versus quiz achievement. That is, how does GALT score affect the decay of knowledge? The two-way interaction effect between GALT group (high,
ARTICLE
medium, and low) and test versus quiz achievement is nonsignificant in all three courses. This means that the previously discussed main effects for test versus quiz achievement for each class hold true regardless of a student’s logical reasoning ability level. For nursing and high school courses, there is no difference from test to quiz score for high, medium, or low GALT students. For the nonscience majors, the significant difference found from test to quiz is significant for students of all GALT levels. In addition, the three-way interaction effect for GALT, quiz time interval, and test versus quiz achievement was found to be nonsignificant for each course. This further demonstrates that time of quiz delay does not differentially affect students of different GALT levels for any course. In the nursing and high school courses, then, high, medium, and low GALT students showed the same nonsignificant decay from test to quiz achievement, regardless of when the quiz was given. One may have originally predicted no decay for high GALT students over time and increasing decay over time for low GALT students, but this hypothesis was not supported by the data. In the nonscience majors’ course, the significant effect for decay of knowledge as seen between test and quiz achievement held true for all students, regardless of whether they had high, medium, or low GALT ability. Overall, logical reasoning ability, as measured by GALT score, does not appear to affect the decay of knowledge of these scores over time.
’ DISCUSSION The results of this study indicate that there is a significant decrease in achievement between test and delayed quizzes for students in at least one general chemistry course. In this experiment, the nonscience majors were the only students to show a significant decrease in achievement for this situation. The decrease is stable across all measured time intervals from 2 to 17 days, which leads to the conclusion that the decay of knowledge for this group occurs within the first 48 h after the testing occasion and is unchanged for more than two weeks. The literature supports the idea that decay of knowledge occurs immediately after a testing occasion.1 The observed knowledge decay appears to be independent of student logical reasoning ability (GALT) and must, therefore, be attributable to some other variable(s). Differences in student motivation in the three general chemistry courses may contribute to the differences in decay of knowledge seen here. Other differences among the three courses include variations in both curricula and course infrastructure. Motivation
It could easily be surmised that the two groups (undergraduate nursing students and high school honors students) that show no significant drop in achievement from test to subsequent quizzes might be more motivated than the one group that did demonstrate a significant drop in achievement (undergraduate nonscience majors). It is reasonable to believe that nursing students who are competing for acceptance into the clinical nursing program would be highly motivated to succeed in a required prerequisite course such as general chemistry. Likewise, high school honors students enrolled in a select private high school would be motivated to attain the highest possible GPA for admission to competitive colleges. Undergraduate nonscience majors, on the other hand, who are enrolled in general chemistry as a distribution elective, might be viewed as not being as motivated relative to the chemistry course as the other two groups. If 1235
dx.doi.org/10.1021/ed100683h |J. Chem. Educ. 2011, 88, 1231–1237
Journal of Chemical Education
ARTICLE
Table 6. Undergraduate Nonscience Majors’ Responses to a Subset of Self-Efficacy Questions on a Course Evaluation Item 1
Before this course started, I was confident that I could learn and understand chemistry.
2
Now that the course is over, I feel confident that I can learn chemistry.
1.8
(Average change in confidence to succeed from pre to post course)
0.9b
6
I enjoyed this class.
1.6
13
I would consider taking a second chemistry course if I had room for another science/math elective.
2.4
14
This course was better than I expected it would be.
1.2
15
This course was worse than I expected.
4.2
On a scale of 1 to 5 (1 = one of the three best courses you have taken and 5 = one of the worst courses), how would you rate this course?
2.0
Free response a
Average Scorea
Statement for Response
2.7
Likert scale of 1 5, with 1 = “agree strongly” and 5 = “disagree strongly”; n = 37. b The change found tends toward “agree strongly”.
this is true, then this lack of comparable motivation might be responsible for a significant decay in knowledge following a scheduled test for the undergraduate nonscience majors. To further investigate the motivation level of the undergraduate nonscience majors, their responses to a teacher-written, anonymous course evaluation were analyzed. According to Glynn et al.,11 motivation is “the internal state that arouses, directs, and sustains students’ behavior towards achieving certain goals”. Motivation can be broken down into five key constructs,12,13 which include self-efficacy. Bandura14 defines self-efficacy as “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments”. To further pursue this idea, Zusho et al.15 investigated and found that student self-efficacy can be used to predict grades in college chemistry courses. In the undergraduate nonscience majors course, student self-efficacy was measured by the students’ responses to seven Likert-scale questions on the end of semester course evaluation. Of the 44 students enrolled in this course, 37 completed the evaluation, yielding an 84% response rate. The seven questions from this evaluation dealing with selfefficacy are included in Table 6. The results for self-efficacy shown in Table 6 suggest that the undergraduate nonscience majors included in this study believed more strongly that they could succeed in chemistry after having completed the course than they did when they started the course. They also report a high level of enjoyment of the course and rate the course as one of the best courses they have taken. From these results, we can interpret their self-efficacy and motivation to succeed in this general chemistry course as high and thus not an easy explanation as to why their achievement dropped significantly within the first 48 h following a test. Curricula
The high school honors and undergraduate nursing curricula both include a spiral curriculum with concepts being introduced and then elaborated from chapter to chapter. The structure is transparent to students owing to the structure provided in the textbook. Goals for each chapter are delineated in the book and reinforced with end of chapter problems. The nonscience majors’ course used a different approach to presenting the spiral curriculum. Here, the real-world issues of global warming, ozone depletion, and others were used to introduce students to the relevant chemistry of these issues. Students may see each chapter as a stand-alone module rather than as a continuous spiraling of chemistry concepts across issues. If students view each chapter as a complete story of both issue and chemistry, they may see very little chemistry carryover to the next chapter. As a result, they
may not attempt to store their knowledge in an easily accessible schema. These students may not then be able to easily access their previous knowledge when studying a new chapter. This could be one reason why this group experienced a decay in knowledge from test to quizzes within the first 48 h following a test. By contrast, the undergraduate nursing and high school honors’ groups may have more easily seen the utility of their knowledge from one chapter to the next. This may explain why there is no significant extinction of their knowledge within two weeks following a test. These students may be using their newly gained knowledge from one chapter in the current chapter they are studying and thus see the value in retaining it. Literature reports1 3 suggest that students also experience a smaller knowledge decay when they are tested repeatedly. The undergraduate nursing course in this study included daily quizzes in addition to the quizzes used in this study. This situation resulted in providing them with a good deal of practice accessing the material. The high school honors students did experience some additional quizzing and were required to submit homework for grading on a regular basis. The nonscience majors did not have any intervening quizzes as part of their course structure. They were, however, required to submit homework for grading for each chapter covered in the course. The undergraduate nursing and high school honors’ general chemistry courses had cumulative final exams. The undergraduate nonscience majors did not. These differences in curricula design (explicit vs implicit spiral curricula, regular quizzing between tests, and the presence of a cumulative final exam) may be responsible for the difference in knowledge decay exhibited by the undergraduate nonscience majors versus the undergraduate nursing and high school honors classes.
’ CONCLUSION AND FUTURE STUDIES This is the first in what could be a series of studies investigating the decay of student knowledge. The goal of this particular study was to investigate if a decay of knowledge following a test does exist and if the decay is found in a variety of general chemistry courses. Future studies could investigate the causes for a decay of knowledge in a more systematic fashion. These causes may include, but not be limited to, the following: the presence or absence of repeated occasions of tests and quizzes; student ability; student motivation; teaching style; interactive versus passive classrooms; spiral versus linear curricula; and the presence or absence of cumulative final examinations. 1236
dx.doi.org/10.1021/ed100683h |J. Chem. Educ. 2011, 88, 1231–1237
Journal of Chemical Education
ARTICLE
Decay of knowledge does occur for some students in some general chemistry courses. This study has highlighted the interaction between decay of knowledge and different general chemistry courses. To minimize the decay of knowledge, teachers should make the continued use of concepts across topics evident to students throughout a course and provide multiple opportunities to practice and apply that knowledge in testing or graded assessments. Literature shows that if knowledge decay does occur, it will happen rapidly yet can be remediated with additional integration and practice opportunities.1 4 Such integration and additional practice opportunities should result in students being given additional opportunities to both improve their knowledge schemas (spiral curriculum) and reinforce their retrieval paths from schemas (multiple testing).
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected]. )
Notes
This work was conducted while at Catholic University of America.
’ REFERENCES (1) Wheeler, M. A.; Ewers, M.; Buonanno, J. F. Memory 2003, 11, 571–580. (2) Rickard, T. C. J. Exp. Psychol. 2007, 33, 297–304. (3) Karpicke, J. D.; Roediger, H. L. Science 2008, 319, 966–968. (4) Anderson, J. R.; Bothell, D.; Byrne, M. D. Psychol. Rev. 2004, 111, 1036–1060. (5) Hockley, W. E. J. Exp. Psychol. 1992, 18, 1321–1330. (6) American Chemical Society. Chemistry in the Community (ChemCom), 5th ed.; W. H. Freeman and Company: New York, 2006. (7) American Chemical Society. Chemistry in Context: Applying Chemistry to Society, 6th ed.; McGraw-Hill Higher Education: Dubuque, IA, 2009. (8) Roadrangka, V.; Yeany, R. H.; Padilla, M. J. GALT, Group Assessment of Logical Thinking; University of Georgia: Athens, GA, 1982. (9) Bunce, D. M.; Hutchinson, K. D. J. Chem. Educ. 1993, 70, 183–187. (10) Pallant, J. SPSS Survival Manual; Open University Press: Philadelphia, PA, 2001. (11) Glynn, S. M.; Taasoobshirazi, G.; Brickman, P. J. Res. Sci. Teach. 2007, 44, 1088–1107. (12) Bandura, A. Ann. Rev. Psychol. 2001, 52, 1–26. (13) Schunk, D. H. In Self-Regulated Learning and Academic Achievement: Theoretical Perspectives, 2nd ed.; Zimmerman, B. J.; Schunk, D. H., Eds.; Erlbaum: Mahwah, NJ, 2001; pp 125 151. (14) Bandura, A. Self-Efficacy: The Exercise of Control; Freeman: New York, 1997. (15) Zusho, A.; Pintrich, P. R.; Coppola, B. Int. J. Sci. Educ. 2003, 25, 1081–1094.
1237
dx.doi.org/10.1021/ed100683h |J. Chem. Educ. 2011, 88, 1231–1237
