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Relationship of Individual Student Normalized Learning Gains in Mechanics with Gender, High-School Physics, and Pretest Scores on Mathematics and Spatial Visualization. * † � � � �

Relationship of Individual Student Normalized Learning Gains in Mechanics
with Gender, High-School Physics, and Pretest Scores on Mathematics and
Spatial Visualization. * † 
Richard R. Hake
Indiana University (Emeritus), 24245 Hatteras Street, Woodland Hills, CA 91367
In a previous survey (Hake 1998a,b; 2002a) the correlation of the average normalized gain <g>
on the FCI (Hestenes et al. 1992, Halloun et al. 1995) or Mechanics Diagnostic (Halloun &
Hestenes 1985a,b) test for 62 courses with %<pretest> was a very low +0.02. However, open
research questions remain as to the existence of "hidden variables," a term evidently first used in
an educational sense by Meltzer (2001). Hidden variables that might influence FCI <g>'s are,
e.g., the averages over a class of gender, math proficiency, spatial visualization ability,
completion of high-school physics courses, scientific reasoning skills, physics aptitude,
personality type, motivation, socio-economic level, ethnicity, IQ, SAT, and GPA. One approach
to this question is to investigate the relationship of individual student learning gains g with such
variables for single courses (Hake et al. 1994, Meltzer 2001), as is done below. One of the goals
of such work is to discover student-profile characteristics or diagnostic tests that might alert
teachers to potential low-g students. If such were known then corrective actions might be taken
early in the course so as to improve its overall effectiveness.
*Partially supported by NSF Grant DUE/MDR-9253965.
The reference is R.R. Hake, "Relationship of Individual Student Normalized Learning Gains in
Mechanics with Gender, High-School Physics, and Pretest Scores on Mathematics and Spatial
Visualization," submitted to the Physics Education Research Conference; Boise, Idaho; August 2002.
Online at < > and also as ref. 22 at < >.
© Richard R. Hake, 6/26/02. Permission to copy or disseminate all or part of this material isgranted
provided that the copies are not made or distributed for commercial advantage, and the copyright and its
date appear. To disseminate otherwise, to republish, or to place at another website (instead of linking to
the above URL) requires written permission.
In the present article I revisit the data for a course "P201" that I team-taught with Professor Rick
VanKooten at Indiana University in the Spring of 1995. This course is an "algebra-based course"
(an obvious oxymoron for any substantive mechanics course) primarily for premeds and health
professionals. The traditional topics for P201 were "covered": Newtonian mechanics, work and
energy, momentum and collisions, solids & fluids, vibrations, and sound. The course is
designated "IU95S" in Table Ic & IIc of Hake (1998b). According to those tables, for this course:
(a) <g> = 0.60 as determined from the <%posttest> = 77 ± 15sd and <%pretest> = 42 ± 15sd
for 209 students, each of whom took both the prettest and posttest. Here and hereafter in this
paper the number after the "± " will always be the standard deviation (sd);
(b) the original Force Concept Inventory (FCI) (Hestenes et al. 1992) was used, except that
minor changes were made in the wording of seven of the questions so as to remove
ambiguities. Neither the scores not the point biserial coefficients for those questions showed
significant changes from the IU93S results in which the test was the original FCI;
(c) the Kuder Richardson reliability coefficient KR-20 for the FCI posttest was 0.81;
(d) interactive engagement strategies included Socratic Dialogue Inducing Labs (Hake
1992), a few Microcomputer-Based Labs (Thornton & Sololoff 1990), Concept Tests
(Mazur 1997), and Minute Papers (Schwartz 1983);
(e) the rather standard introductory physics text by Serway & Faughn (College Physics, 4th
edition) was utilized.
In the research discussed below we consider the scores of only the 203 students (93 females and
110 males who took all four diagnostic tests:the FCI pretest, FCI posttest, a math test, and a
spatial visualization test. Thus the group considered does not include all the 209 students
considered in Hake (1998a,b). Both the math test and the spatial test were given to the students at
the start of the course. The math test was the Indiana University "Math Skills Assessment"
(MSA) test, mostly simple algebra. As of 1995, the MSA was normally given to incoming
freshman at Indiana. The spatial test was the Purdue Spatial Visualization (Rotations) test (Guay
Table I summarizes the data of the present study. In the table F = Female, M = Male, and A =
All students. Consistent with Hake (1998a,b; 2001a, 2002a) the following parameters are
(1) g is the single-student normalized gain, defined as:
g = %Gain / %Gainmax . . . . . . . . . . . . . . . . . . . . . . . . . (1a)
g = ( %posttest – %pretest) / (100 – %pretest) . . . . . . . . . . . . . . . . . . . . . . (1b)
(2) <g> is the course average normalized gain, defined as the actual average gain, %<Gain>,
divided by the maximum possible actual average gain, %<Gain>max:
<g> = %<Gain> / %<Gain>max. . . . . . . . . . . . . . . . . (2a)
<g> = ( %<posttest> – %<pretest>) / (100 – %<pretest>) . . . . . . . . . . . . . . . (2b)
where %<posttest> and %<pretest> are the final (posttest) and initial (pretest) class percentage
(3) g-ave is the course average normalized gain calculated as the average of the single-student
normalized gains gi :
g-ave = (1/N) i g i = (1/N) i [(%post
i – %pre
i ) / (100% – pre
i)] . . . . . . . (3)
where N is the number of students taking both the pre- and post-tests and the summation is
over all N students. The advantage of using <g> rather than g-ave in a survey of course
performance (Hake 1998a,b) is discussed in Hake (2001a). In the present work g-ave has an
advantage in allowing calculation of standard deviations (sd's) of g for students within a single
(4) "d" is Cohen's (1988) "effect size" defined as
d = (mA m B) / [(sd 2A+ sdB
2)/2]1/2 . . . . . . . . . . . . . . . . . . . . . . . . . . (4)
where mA and mB are population means expressed in the raw (original measurement) unit,
and where the denominator is the root mean square of standard deviations for the A- and B-group
means, sometimes called the "pooled standard deviation." Cohen's (1988, p. 24) very rough rule
of thumb – based on typical results in social science research – is that d = 0.2, 0.5, 0.8 imply
respectively "small," "medium," and "large" effects, but Cohen cautions that the adjectives "are
relative, not only to each other, but to the area of behavioral science or even more particularly to
the specific content and research method being employed in any given investigation."
(5) HSP stands for High School Physics and <gh> is a hypothetical <g> for high-school courses
attended by the IU95S students as calculated from
<gh> = ( %<pre-HSP> – %<preNoHSP>) / (100 – %<preNoHSP>) . . . . . . . (5)
as discussed in (Hake 2000). Here I assume, as a rough approximation, that the HSP graduates
would have averaged %<Pre-HSP> just after HSP, and would have averaged %<preNoHSP>),
just before they took HSP.
Table I. Parameters for Course IU95S.
Among the interesting features of Table I are, in order of descending rows:
a. The course average normalized gain <g> = 0.604 as calculated from the averages of the pre-
and post-tests (Eq. 2a,b) is 3% lower than the course average normalized gain g-ave = 0.622
calculated from the average of the single-student normalized gains (Eq. 3). This difference is
consistent with Hake (1998a, footnote 46), where it is shown that [g-ave – <g>] is proportional
to the gj-weighted average of the deviations (prej
– <prej>). Since the average of (prej
– <prej> ) is zero,
a low [g(ave) – <g>] implies a low correlation between g j
and prej
for individual students,
just as there is a low correlation between <g> and <pre> for courses as indicated in the
"Introduction." The fact that these two types of averages are generally within 5% of one another
for classes of about 20 students or more is a consequence of the relatively low correlation of
single-student g's with pretests scores. Table II indicates that for IU95S, that correlation is a
relatively low +0.32.
b. The average normalized gain <g> = 0.604 is somewhat larger than the average
<<g>>48IE = 0.48 ± 0.14 (std dev) of forty-eight Interactive Engagement courses (N = 4458)
surveyed by Hake (1998a,b). The second row of the table indicates that students who had
completed High School Physics (HSP) achieved a larger g-ave than students who did not, but the
effect size is only 0.19 and the very low hypothesized normalized gain gh = 0.11 (bottom row)
[less than the <g>14T-ave = 0.23 ± 0.04 of 14 Traditional courses (N= 2084)] suggest that the g-ave
differential may be due more to the fact that high-school students who take physics tend to be
more scientifically oriented than to the education per se received in their high-school physics
courses. (As usual, corr
h elation doesn't necessarily indicate causation.) The low gh may reflect:
(1) A rapid decrease in physics understanding in the years following HSP, as might be
expected if only incoherent and loosely related bits of physics understanding had been
(2) A failure of HSP to impart much understanding of physics in the first place.
(3) Some combination of "1" and "2".
In any case, the results suggest the ineffectiveness of HSP to promote long-term conceptual
understanding and the need for improved physics education of K-12 teachers (Hake 2000;
c. The average math-test score, %math-ave, for All the students (average level – midway
between sophomore and junior) was 64.5%, close to the 62% usually taken to indicate that a
student is at risk in substantive introductory math and science courses. The average math scores
for males were higher than those for females with a modest effect size of 0.31. Students who had
completed High School Physics (HSP) achieved a larger %math-ave than students who did not (d
= 0.34), but here again the differential is probably due more to the fact that high-school students
who take physics tend to be more mathematically oriented than to the math education per se
received in HSP.
d. Average scores on the spatial test, %spatial-ave, for males were higher than those for females
with a "large" effect size of 0.82, consistent with previous investigations (Pallrand & Seeber
1984, Linn & Peterson 1985, Lord 1987, Howe & Doody 1989, Baenenenger & Newcombe
1989, Halpern 1992, Friedman 1995,). This gender difference is often attributed to cultural
factors (e.g., boys are more inclined than girls to play with construction toys, engage in motion-
intensive sports, and play computer games). Consistent with this assumption, Baartmans &
Sorby (1996) showed that women engineering students at Michigan Technological University
could perform as well as men on spatial visualization tests if brought up to speed by a one-
quarter (6 hr/week) visualization course based on the Baartmans & Sorby text.
e. Males achieved larger g-ave = 0.688 than females with g-ave = 0.543, with a "close to large"
effect size 0.68. As discussed in Hake (2002a); Henderson et al. (1999), McCullough (2000),
Galileo Project (2002), and Meltzer (2001) have also reported g-avemales > g-avefemales for some
classes. For an abridged version of the Conceptual Survey of Electricity (Maloney et al. 2001),
Meltzer calculated gender-difference effect sizes of 0.44 and 0.59 for two classes [N = 59, 78] at
Iowa State University, but observed no significant gender difference in two other classes [N =
45, 37] at Southeastern Louisiana University.)
The scatter plots of Fig. 1, display the gender-related spread in the pre/post FCI data.
Fig, 1. Scatter plots of FCI %Gain vs. FCI pretest for the 93 females and
110 males of IU95S. The blue diamonds, purple squares, and brown
triangles indicate, respectively, one, two, and three juxtaposed data points.
The lines with negative slopes g = 0.42 and g = 0.78 form the boundaries of
the high gain (HG) and low gain (LG) regions as explained in the text.
As in earlier work (Hake et al. 1994), I arbitrarily define for the present course the regions:
(a) "high gain" (HG) as g > 1.3 <g> = 1.3 (0.60) = 0.78; containing 8 females and 39 males;
(b) "low gain" (LG) as g < 0.7 <g> = 0.7 (0.60) = 0.42; containing 25 females and 12 males.
Thus, although females comprise 46% of the enrollment, they constitute 68% of the LG's and
only 17% of the HG's. For comparison, in an earlier course IU94S, Hake et al. (1994) found that
females, who comprised 47% of the enrollment, constituted 71% of the LG's and (in sharp
contrast to IU95S) 54% of the HG's. It's possible that the superior PER-based text by Reif (1994)
and the slower pace (only mechanics was "covered") contributed to the higher percentage of
female HG's in IU94S with its <g> = 0.65.
According to educational psychologist Greg Schraw's (1998) PERC 1998 paper:
"There's a lot of snobbery, at least in quantitative research, and true . . .(physicists would require
quotes around the education-specialist's use of "true") . . . . experiments are always viewed as
sort of the ideal. Correlational studies are viewed as trash, and quasi-experiments . . . (neither
random selection nor assignment of subjects as in "true" experiments – see Cook & Campbell
1979). . . are somewhere in the middle. . . . there’s too much correlational research out there
already, and we shouldn't promote any more of it than we have to." (My italics.)
So why am I about to talk "trash"? Because the correlations of Table II are part of an in-depth
quasi-experimental study (Hake 1998a,b; 2002a) that, according to Schraw, "is exactly the kind
of research that science educators need."
Table II. Correlations for IU95S.
Table II shows correlations for All, Female, and Male studcnts between single student's (a)
normalized gain g and math, spatial, and pretest scores; (b) posttest and pretest scores; and (c)
gain and pretest scores. Among features of interest are:
(a) The correlation r = 0.36 between g's and math score is similar to the correlations observed by
Meltzer (2001): 0.38 (N=45), 0.10 (N = 37), 0.46 (N = 59) , and 0.30 (N = 78) in four courses
using an abridged version of the Conceptual Survey of Electricity (Maloney et al. 2001).
(b) The correlation r = 0.24 between g's and spatial score would be classed by Cohen as "small."
It is interesting that the correlation between female g's and spatial score is a very low 0.06.
(c) The correlations of +0.58 between post tests and pretest scores, and –0.44 between gains and
pretest score for single students are similar to those observed by Hake (1998a) for courses. The
correlation of (%<posttest>) with (%<pretest>) was + 0.55, and the correlation of %<Gain>)
with %<pretest> was –0.49.
The relatively modest correlations between g's and math, spatial, and FCI pretest scores (Table
II) indicate that the tests used in this study cannot, by themselves, definitively identify potential
low gainers. Likewise, the low effect size for dependence of g's on completion of high-school
physics courses (Table I, second row) indicates that the completion of HSP is also, by itself, a
poor indicator of university FCI gain. Nevertheless I suspect that relatively low scores on all
three tests, or pathologically low scores on one or more of the tests might serve to identify
potential LG's.
Possible intervention strategies for potential LG's are:
(a) Urge students with low diagnostic math scores to brush up. Some may need tutoring.
(b) Urge students who did not take HSP to exert extra effort, e.g., attend help sessions, join
study groups, seek help from classmates and instructors.
(c) Interview students with relatively low scores on all three tests, or pathologically low
scores on one or more of the tests to uncover serious cognitive or affective problems. If
possible recommend a help menu.
(d) To enhance the spatial visualization of all students (not just potential LG's) meld some
Baartmans & Sorby strategies into the course. Do more lab experiments such as the conical
pendulum of Socratic Dialogue Inducing Lab #3 "Circular Motion and Frictional Forces,"
(Hake 1998c) that require 2D representations of 3D motion.
A salient result of the present research is the demonstration of gender disparity in normalized
gains [Table I (row 2) and Fig. 1]. The effect size d = 0.68 is not far from the d = 0.8 that Cohen
loosely designates as "large." However, this gender effect size is eclipsed by the very large d =
2.43 (Hake 2002a) for interactive engagement vs traditional courses in the survey of Hake
(1998a,b). [Seven reasons for this unusually large d are given in Hake (2002a).] Thus, in my
opinion, effort to increase the degree of effective interactive engagement for ALL students should
probably take a higher priority than effort to reduce gender disparity in FCI <g> values, even
despite the deplorable gender inequity in physics participation (Mallow & Hake 2002).
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... (2) Description: NP : The percent value sought or expected R : The number of scores obtained by students or groups NS : Maximum total score The improvement of students' scientific performance can be seen from the initial and final performance scores using the N-Gain formula. To find out the increase in scientific work, the N-Gain formula is used as follows: Test the effectiveness of electronic learning chemistry assessment application using the effect size test using the formula (Hake, 2002): ...
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Today the electronic learning has developed because in line with the demands of the that sorely on technology. Assessment is one of the important elements in instructional activities and to support e-learning appear the electronic assessment. This study aims to develop an electronic learning chemistry assesment (Element) digital application through project based learning (PjBL) on the concept of applied chemistry. The research method used is the research and development method. The research subjects for the product trial developed in this study were students of the class of 2019/2020 with a purposive sampling technique with a sample of 24 students. This research instrument uses a needs analysis questionnaire, expert validation before and after revision, student response and small-scale trials. The assessment component contained in the application consists of quiz assignments, practical, product, project and final. The validation results were analyzed using content validity ratio (CVR) analysis and each criterion was then calculated the content validity index (CVI) or average value. The results showed that the Element through PjBL on applied chemistry concepts had been validated by material and media experts with an average CVI of 0.9 or the very valid category. The results of the small-scale test of the electronic-virtual assessment for of learning application for students obtained an average percentage of 93.9% with the criteria of "very interesting". Furthermore, the data on increasing students' scientific performance has increased by 0.70 with a high category and is strong effectively used to improve student scientific performance
... ≤ <g> ≤ .7 (Moderate) and < .3 (Low) (Hake, 2002). The categorizations of scientific literacy are as follows: 0-20 = very low, 21-40 = low, 41-60 = moderate, 61-80 = high, and 81-100 = very high. ...
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p style="text-align: justify;">Scientific literacy is a critical competency for people to take an essential role in science, technology, and social advancement. It is important to note that this competence is still a problem for most students worldwide. Therefore, this study analysed students' scientific literacy differences between a project-based learning flipped classroom (PjBL-FC) and a project-based learning (PjBL) class assisted by learning resources in wetlands environments. This quasi-experimental study used a non-equivalent control group design involving Class X Senior High School as the sample. The data were inferentially analysed by t-test. The results showed that the scientific literacy of students in the class that applied the PjBL-FC was better than those who applied only PjBL. Furthermore, all the indicators reach the high to very high category except the ability to propose a hypothesis, which is in the medium category. It was concluded that flipped classroom makes the PjBL take place more efficiently and effectively. Further studies can be carried out to determine how students use the learning materials, how teachers design the PjBL strategy in an online platform, their effect on scientific literacy, and how to combine PjBL with other approaches.</p
... We note that in physics, students' physics conceptual understanding is an important academic outcome. However, prior studies showed that female students often have lower average scores than male students on physics concept inventories [105][106][107][108][109]. For example, a prior study showed that men, on average, outperform women on the mechanics conceptual inventories by 13% on the pretest and by 12% on the posttest [17]. ...
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We discuss an investigation of students’ motivational beliefs and performance on the Force Concept Inventory (FCI) in a calculus-based introductory physics course at a large public university in the U.S. We investigated how students’ perception of the inclusiveness of the learning environment (including perceived recognition, perceived effectiveness of peer interaction, and sense of belonging) predicts students’ FCI scores and physics motivational beliefs (including self-efficacy, interest, and overall physics identity) at the end of the course after controlling for students’ high school performance and their FCI scores and motivational beliefs at the beginning of the course. We find signatures of noninclusive learning environment in that female students’ mean scores in physics motivational beliefs and perception of the inclusiveness of the learning environment were lower than male students’, and the gender gap in students’ self-efficacy increased from the beginning to the end of the course. Using structural equation modeling, we find that the gender differences in students’ motivational beliefs and FCI scores were mediated by the different components of students’ perception of the inclusiveness of the learning environment. In particular, students’ perceived recognition, e.g., by instructors, was an important predictor of their overall physics identity, and their sense of belonging predicted their self-efficacy and FCI scores. Our findings can be valuable for contemplating guidelines for creating an inclusive learning environment in which all students can excel.
... Tes Hake dilakukan untuk membandingkan sampel antara pretest dan posttest pada instrumen yang sama (Archambault, dkk, 2008). Persamaan rumus dari uji N-Gain adalah sebagai berikut (Hake, 2002): (Hake, 1998). ...
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Critical thinking is a cognitive ability to solve and make decisions in complex situations in everyday life. This study aims to find out the influence of mobile applications on students’ ability of critical thinking in physics. This type of research is an experiment using quantitative methods. The subjects of this study were students from SMA Negeri 1 Nanga Pinoh grade 10th, a total of students are 31 people. This study was used a test technique in data collection and the test instrument used is in the form of pretest and posttest essay questions, which contain three aspects of critical thinking with twelve indicators. Aspects of critical thinking, namely: the effective reasoning, using a system of thinking, and making judgments and decisions. The data analysis technique used is descriptive analysis, simple linear regression test and N-Gain test. The conclusion in this study is students' critical thinking in physics before and after using this mobile application in the critical category. The use of mobile applications only affects 22.15% of students' critical thinking skills in physics. Changes in students' critical thinking skills after using mobile applications are low.Keywords: Critical thinking, mobile app, physics, studentsAbstrak: Berpikir kritis merupakan kemampuan kognitif untuk memecahkan dan membuat keputusan dalam situasi yang kompleks dalam kehidupan sehari-hari. Tujuan penelitian ini adalah mengetahui pengaruh penggunaan aplikasi mobile terhadap kemampuan berpikir kritis peserta didik dalam fisika. Jenis penelitian berupa eksperimen dengan menggunakan metode kuantitatif. Subjek penelitian ini adalah peserta didik SMA Negeri 1 Nanga Pinoh kelas X MIPA 1, dengan jumlah 31 orang. Penelitian ini menggunakan teknik tes dalam pengumpulan data dan instrumen tes yang digunakan berupa soal esai pretest dan posttest, yang berisi tiga aspek berpikir kritis dengan dua belas indikator. Aspek-aspek dari berpikir kritis, yakni: alasan yang sangat efektif, menggunakan suatu sistem berpikir, serta membuat suatu penilaian dan keputusan. Teknik analisis data yang digunakan adalah analisis deskriptif, uji regresi linier sederhana dan uji N-Gain. Simpulan dalam penelitian ini adalah kemampuan berpikir kritis peserta didik dalam fisika sebelum dan sesudah menggunakan aplikasi mobile adalah sama dalam kategori cukup kritis. Penggunaan aplikasi mobile berpengaruh hanya 22,15% pada kemampuan berpikir kritis peserta didik dalam fisika. Perubahan kemampuan berpikir kritis peserta didik sesudah menggunakan aplikasi mobile tergolong rendah.Kata-kata kunci: Berpikir kritis, aplikasi mobile, fisika, peserta didik
... The null hypothesis for these tests asserts that the median pre or post students confidence levels of the two samples (control and intervention) are identical and a p-value < 0.05 was used to reject the null. Additionally, we used a confidence gain metric similar to Hake's learning gain metric [4,9] as there was a significant difference between the pre-confidence levels of our control and intervention cohorts. The confidence gain metric would account for cohorts that may have higher confidence than others when they begin the semester and it was computed as: ...
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This experience report describes and evaluates the introduction of Hire Thy Gator technical interview preparation activities in a Data Structures and Algorithms (DSA) course. Our intervention included a panel on internship experiences, a role-play interviewing demonstration, two participatory mock interview preparation exercises where students interviewed each other first using self-selected peers and second through random pair-ups, and graded short programming problems. We (1) explain the logistics and rationale for embedding these activities, (2) describe the lessons learned and evolution of the activities beyond the intervention semester, and (3) evaluate the impact of these activities on students. We report data from 257 students who participated in our intervention and 106 students who were a part of a control group. Students found that our activities promoted awareness of the recruitment process, allowed them to self-evaluate their strengths and weaknesses, and prepared them for technical interviews. Quantitatively, the intervention cohort reported a higher average normalized confidence gain (0.42) than the control group (0.36) indicating that our activities can aid in building students' confidence. Our work contributes rich descriptions of interview preparation activities which can be used by instructors in computing courses.
Digital literacy is a vital competency needed by physics teachers, so it should be trained through lectures using technology, one of which is LMS3. The study aims to describe the LMS3 implementation in school physics lectures to train digital literacy and elucidate an enhancement of prospective physics teachers’ digital literacy in school physics lectures using LMS3. One-group pre-test–post-test design was conducted on 38 prospective physics teachers who enrolled in school physics lectures. The instruments consisted of digital literacy tests, lecture implementation forms, and interview guides. Besides, LMS3 is an application designed independently using the ADDIE (analysis, design, development, implementation, and evaluation) development model. The learning process using LMS3 occurred synchronously after the lecturer activated the learning process menu and adopted problem-based learning syntax. In general, the enhancement of digital literacy of prospective physics teachers after attending school physics lectures using LMS3 was the high category (N-gain = 0.72). In conclusion, school physics lectures using LMS3 had a significant impact on enhancing digital literacy. This study suggests developing lectures using various technologies that support the improvement of teacher competence in facing challenges in education.
The aims of this research were to determine students’ learning outcome before and after the learning application based on Science, Environment, Technology and Society (SETS) and to assess the effectiveness of learning application based on SETS towards grade eighth students’ learning outcome of SMPIT Nurul Wahdah Pontianak. The form of this research was preexperimental design with one-group pre-test-post-test design. The subject of this research was 31 students of grade eighth. Data collection technique were measurement, direct communication, and observation. The collected data were analyzed using normality test with Shapiro-Wilk test. The normality test result showed that the Sig. values of pre-test and post-test were more than 0.05 which means that data were distrubuted normally. The result of hypothesis test using Paired Sample T-Test showed that Sig. (2-Tailed) value were 0.000 which is less than 0.05. It means that there are differences between pre-test and post-test result. The learning application based on SETS effective enough to increase the students' learning outcome with N-Gain interpretation by 74%.Keywords: Effectiveness, Learning Outcome, SETS
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An instrument to assess the basic knowledge state of students taking a first course in physics has been designed and validated. Measurements with the instrument show that the student's initial qualitative, common sense beliefs about motion and causes has a large effect on performance in physics, but conventional instruction induces only a small change in those beliefs.
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Microcomputer-based laboratory (MBL) tools have been developed which interface to Apple II and Macintosh computers. Students use these tools to collect physical data that are graphed in real time and then can be manipulated and analyzed. The MBL tools have made possible discovery-based laboratory curricula that embody results from educational research. These curricula allow students to take an active role in their learning and encourage them to construct physical knowledge from observation of the physical world. The curricula encourage collaborative learning by taking advantage of the fact that MBL tools present data in an immediately understandable graphical form. This article describes one of the tools-the motion detector (hardware and software)-and the kinematics curriculum. The effectiveness of this curriculum compared to traditional college and university methods for helping students learn basic kinematics concepts has been evaluated by pre- and post-testing and by observation. There is strong evidence for significantly improved learning and retention by students who used the MBL materials, compared to those taught in lecture.
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Common sense beliefs of college students about motion and its causes are surveyed and analyzed. A taxonomy of common sense concepts which conflict with Newtonian theory is developed as a guide to instruction.
There have been many investigations into the factors that underlie variations in individual student performance in college physics courses. Numerous studies report a positive correlation between students' mathematical skills and their exam grades in college physics. However, few studies have examined students' learning gain resulting from physics instruction, particularly with regard to qualitative, conceptual understanding. We report on the results of our investigation into some of the factors, including mathematical skill, that might be associated with variations in students' ability to achieve conceptual learning gains in a physics course that employs interactive-engagement methods. It was found that students' normalized learning gains are not significantly correlated with their pretest scores on a physics concept test. In contrast, in three of the four sample populations studied it was found that there is a significant correlation between normalized learning gain and students' preinstruction mathematics skill. In two of the samples, both males and females independently exhibited the correlation between learning gain and mathematics skill. These results suggest that students' initial level of physics concept knowledge might be largely unrelated to their ability to make learning gains in an interactive-engagement course; students' preinstruction algebra skills might be associated with their facility at acquiring physics conceptual knowledge in such a course; and between-class differences in normalized learning gain may reflect not only differences in instructional method, but student population differences ("hidden variables") as well.
The Conceptual Survey of Electricity and Magnetism (CSEM) was developed to assess students' knowledge about topics in electricity and magnetism. The survey is a 32-question, multiple-choice test that can be used as both a pretest and posttest. During four years of testing and refinement, the survey has been given in one form or another to more than 5000 introductory physics students at 30 different institutions. Typical pretest results are that students in calculus-based courses get 31% of the questions correct and student's in algebra/trigonometry-based courses average 25% correct. Posttest correct results only rise to 47% and 44%, respectively. From analysis of student responses, a number of student difficulties in electricity and magnetism are indicated.
An extremely lucid introduction to physics and reasoning methods containing a judicious selection and sequencing of material that enables students to learn without being overwhelmed and acquire important knowledge for future work. Provides detailed instruction of a problem-solving strategy for both quantitative and qualitative problems. Consists of two closely coordinated parts--the text is designed to present basic subject matter as well as facilitate reference and review; the workbook ensures that students have understood what they have read, can interpret it and apply the information to diverse situations.
I. OVERVIEW. 1. Introduction. 2. Peer Instruction. 3. Motivating the Students. 4. A Step-by-Step Guide to Preparing for a Peer Instruction Lecture. 5. Sample Lecture. 6. Epilogue. II. RESOURCES. 7. Mechanics Baseline Test. 8. Force Concept Inventory. 9. Questionnaire Results. 10. Reading Quizzes. 11. Concept Tests. 12. Conceptual Exam Questions. Appendix: Disk Instructions. Index.