In this study we investigate how prior knowledge, the comprehensive learning approach, problem-based teaching and assessment influence students’ basic-learning skills in Mathematics at the university level. To do so, we employed a quasi-experimental research design and a structured questionnaire. Two experimental groups and two control groups of students were involved. We found a negligible correlation between prior knowledge and basic-learning skills but a positive correlation between prior knowledge and the comprehensive learning approach. On the other hand, we found practically no correlation between prior knowledge and assessment. We also found that problem-based teaching correlated positively and that the traditional approach correlated negatively with prior knowledge. Moreover, prior knowledge, problem-based teaching, the comprehensive learning approach and assessment explained 50% of the variance in the levels of basic-learning skills.
El objeto de este estudio es investigar la influencia del conocimiento previo del alumnado, el enfoque de aprendizaje integral, la enseñanza basada en problemas y el impacto de la evaluación en el aprendizaje básico del estudiante de matemáticas a nivel universitario. La investigación plantea un diseño de investigación cuasi experimental y un cuestionario estructurado. En él participaron dos grupos experimentales y dos grupos de control. Los resultados muestran que existe una correlación no significativa entre el conocimiento previo y la habilidad de aprendizaje básico; también plantea que un enfoque de aprendizaje integral se correlaciona positivamente con el conocimiento previo, mientras que con la evaluación casi no existe correlación, si bien existe una correlación positiva con la enseñanza basada en problemas. El enfoque tradicional se correlaciona negativamente con el conocimiento previo. Otros resultados que se obtienen es que el conocimiento previo, la enseñanza basada en problemas, el enfoque de aprendizaje integral y el impacto de la evaluación explican el 50% de la variación en los niveles de habilidades de aprendizaje básico.
El propòsit d'aquest estudi és investigar la influència del coneixement previ de l'alumnat, l'enfocament d'aprenentatge integral, l'ensenyament basat en problemes i l'impacte de l'avaluació en l'aprenentatge bàsic de l'estudiant de matemàtiques a nivell universitari. La investigació planteja un disseny quasiexperimental i un qüestionari estructurat. Hi van participar dos grups experimentals i dos grups de control. Els resultats mostren que hi ha una correlació no significativa entre el coneixement previ i l'habilitat d'aprenentatge bàsic. L'estudi també va revelar que un enfocament d'aprenentatge integral es correlaciona positivament amb el coneixement previ, mentre que amb l'avaluació gairebé no hi ha correlació. D’altra banda, s'observa que hi ha correlació positiva amb l'ensenyament basat en problemes. L'enfocament tradicional es correlaciona negativament amb el coneixement previ. La investigació també planteja que el coneixement previ, l'ensenyament basat en problemes, l'enfocament d'aprenentatge integral i l'impacte de l'avaluació expliquen el 50% de la variació en els nivells d'habilitats d'aprenentatge bàsic.
Mathematics proficiency has always been a challenge for lecturers and students at university. Many researchers have investigated the factors related to students’ achievements in mathematics, such as mathematics preferences, teachers’ knowledge and teacher’s behavior, as well as students’ thinking and learning, and cognitive complexity.
The researcher examined and revealed that a comprehensive learning approach correlates positively with knowledge conceived, as well as half of the variance of basic-learning skill levels is explained by prior knowledge, problem-based teaching, comprehensive learning approach, and assessment impact. The researcher also found that problem- based teaching is making a significant positive contribution, meanwhile, the traditional approach is making a significant but negative contribution to the prediction of basic-learning skill.
While students’ achievements in mathematics has proven to be the most challenging experience for students, as well as the lecturers, mathematics departments might pre-test prior knowledge of students to strengthen their work to increase their academic success in mathematics, as well as might promote the comprehensive learning approach, and. problem-based teaching. There is a need to investigate the influence of other variables on basic-learning skill in mathematics.
The prior mathematics knowledge, comprehension learning approach, as well as problem-based teaching used by teacher and assessment impact, are supposed to be the important variables that influence knowledge conceived in mathematics. The main aim of the study was to investigate the impact of prior knowledge, comprehensive learning approach, problem-based teaching and assessment impact on student's basic-learning skill in mathematics. Learning is to be student‐initiated and that ample time for self‐study should be available (
The fundamental factors in the educative process are an immature, undeveloped being; and certain social aims, meanings, values incarnate in the matured experience of the adult. The educative process is the due interaction of these forces. Abandon the notion of subject- matter as something fixed and ready-made in itself, outside due child's experience; cease thinking of the child’s experience as also something hard and fast; see it as something fluent, embryonic, vital; and we realize that the child and the curriculum are simply two limits which define a single process. The present standpoint of the child and the facts and truths of studies define instruction (
Constructivism is grounded in the concept that learners construct their understanding through experiences and interpretations (
Prior mathematical knowledge in secondary education is thought to be one of the most important premises to obtain good grades in mathematics in the university studies. Many authors have done a lot of research to investigate the association between prior knowledge in mathematics and basic-learning skill in university studies.
Prior knowledge had a positive influence on new learning (
The comprehensive learning approach is assumed to be one of the important variables that impact basic-learning skill in mathematics at the university. A lot of research is carried out to investigate the association between comprehensive learning approach in mathematics and basic-learning skill at university. (
Problem-based teaching used by math teachers is supposed to be one of the most important variables that impact basic-learning skill in mathematics. A lot of research has been done to investigate the association between problem-based teaching in mathematics and basic-learning skill at university.
Collaborative problem-solving performance is positively related to performance in the core PISA subjects (science, reading and mathematics), but the relationship is weaker than that observed among those other domains (
(
Knowledge assessment of students is one of the important variables that are related to basic-learning skill in mathematics. A lot of research is carried out to investigate the association between assessment impact in mathematics and basic-learning skill in the university studies.
Assessment methods and matriculation examination score were the predictors of students’ progress (
Assessment support the advancement of teaching and learning (
Many researchers have done a lot of work to investigate the impact of prior knowledge, comprehensive learning approach, problem-based teaching, and assessment impact on basic-learning skill in mathematics. Approaches to instruction and curriculum design, formative assessment data and feedback to students (
The quantitative approach was the method used in the research. A quasi-experimental research design was used. Quasi-experimental designs do not include the use of random assignment. Researchers who employ these designs rely instead on other techniques to control, or at least reduce threats to internal validity (
In the quasi-experimental research design, the matching-only design was used in the study. The matching-only design differs from random assignment with matching only in the fact that random assignment is not used. The researcher still matches the subjects in the experimental and control groups on certain variables, but he has no assurance that they are equivalent on others. When several groups are available for a method study and the groups can be randomly assigned to different treatments, this design offers an alternative to random assignment of subjects. After the groups have been randomly assigned to the different treatments, the individuals receiving one treatment are matched with individuals receiving the other treatments (
Teaching method was selected to be used as a manipulated variable: traditional vs problem- based teaching, controlling the moderator, mediator and extraneous variables: teaching, curriculum, climate, class management, and technology as a teaching tool.
Two experimental and two control groups of freshman and sophomore students as a non-random sample were selected to be investigated in the research. The two experimental groups of students: finance- bank, business- administration (N=135) were selected in the economic faculty of the university. Meanwhile, the two control groups of students: economical informatics, and information technology (N= 121) were selected in information technology and innovation faculty of the university. The four groups of respondents, except the fact that are from different faculties, they were taught the same syllabus of mathematics curriculum. Relating to classification at university, 81 respondents from experimental groups (60%) were freshman, and 54 respondents (40%) were the sophomore students. Meanwhile, 56 respondents from control groups (46.3%) were freshman, and 65 respondents (53.7%) were the sophomore students. Two experimental groups sample of respondents is composed by 18 females (13.3%), and 117 (86.7%) males; meanwhile two control groups sample of respondents is composed by 32 females (26.4%), and 89 (73.6%) males. Relating to the choice of career, 90 respondents (66.7%) from experimental groups studied in the first choice, 27 (20%) in the second choice, 9 respondents (6.7%) in the third choice, and 9 respondents (6.7%) in the fourth choice. Meanwhile, 40 respondents (33.1%) from control groups studied in the first choice, 32 (26.4%) in the second choice, 24 respondents (19.8%) in the third choice, and 25 respondents (20.7%) in the fourth choice.
A structured questionnaire was used to gather the primary data from the students in the 2018- 2019 academic year. The questionnaire is based on CEVEAPEU questionnaire- an instrument to assess the learning strategies of university students (
Alfa Cronbach values of questionnaire scales vary from .83 to .91 confirming a very good value of reliability, as following
N0 |
Variables |
|
Evaluation |
---|---|---|---|
1 |
Prior knowledge |
.89 |
Good |
2 |
Comprehensive learning approach |
.91 |
Excellent |
3 |
Problem-based teaching |
.88 |
Good |
4 |
Assessment impact |
.85 |
Good |
5 |
Basic-learning skill |
.83 |
Good |
Central tendency values, as well as frequency values, were used to describe the prior mathematics knowledge, comprehensive learning approach, problem-based teaching, assessment impact, and knowledge conceived in mathematics for both, experimental and control groups. Pearson product-moment correlation coefficient was used to assess the relationship between prior mathematics knowledge, comprehensive learning approach, problem-based teaching, assessment impact, and knowledge conceived in mathematics. Linear multivariate regression was used to assess the ability of one control measure to predict knowledge conceived in mathematics by prior knowledge, comprehensive learning approach, problem-based teaching, and assessment impact. Preliminary assumption testing was conducted to check for normality, linearity, outliers, homogeneity of variance-covariance matrices, and multicollinearity, with no violations noted.
Frequencies and central tendency values of prior knowledge, comprehensive learning approach, problem-based teaching, assessment impact, and basic-learning skill variables for experimental and control group of respondents are shown below. The statistical tables with frequencies and central tendency values of main variables are shown in the annexes. The more detailed frequencies of main variables according to gender and study program as crosstabs tables are shown in the annexes too.
Prior knowledge’ frequencies indicates that most of the respondents (46.7%) of the experimental groups and 40.5% of the control groups passed in the previous academic year; 33.3% of the experimental groups and 39.7% of the control groups achieved good results; meanwhile 20% of the experimental groups and 19.8% of the control groups achieved high results. Central tendency values for experimental groups (M= 2.733, SD = .774), as well as for control groups (M= 4.00, SD = .897), indicate the same tendency for values as measured by frequencies. Hence, there are small differences of prior knowledge (pass: 6.2%; good: -6.4%; high: 0.2%) between the experimental and control groups of students. Therefore, there are small differences of prior knowledge between the experimental and control groups of students.
Comparing prior knowledge from previous academic year with current knowledge of experimental group has resulted that: (1) 5.93 % more students fail; (2) 2.96% more students pass, (3) 4.44% less students achieved good; (4) 4.44% less students achieved outstanding results in mathematics. Comparing prior knowledge from previous academic year with current knowledge of control group scores has shown that: (1) 9.09 % more students fail; (2) 6.61% more students pass, (3) 8.27% less students achieved good; (4) 7.43% less students achieved outstanding results. Hence, there is a difference between experimental and control group of students, especially in fail and outstanding levels achieved in mathematics.
Comprehensive learning approach’ frequencies indicates that most of the respondents (53.4%) of experimental groups and 47.1% of control groups include full information from class reading material, practical, etc. during learning; 13.3% of the experimental groups and 26.4% of the control groups include a little; meanwhile 33.3% of the experimental groups and 26.4% of the control groups are undecided. Central tendency values for experimental groups (M= 3.46, SD = .808), as well as for control groups (M= 2.79, SD = .751) indicate the same tendency for values as measured by frequencies. Therefore, there are small differences of comprehensive learning approach (fully include: 6.3%; a little include: -13.1%; undecided: -6.9%) between the experimental and control groups of students.
Problem-based teaching’ frequencies indicates that most of the respondents (66.7%) of the experimental groups and 19.8% of control groups have acquired fully support by lecturers in every lesson during teaching; 6.7% of the experimental groups, and 60.4% of the control groups a little support; meanwhile 26.7% of the experimental groups and 19.8% of the control groups are undecided. Central tendency values for experimental groups (M= 3.86, SD = .887), as well as for control groups (M= 3.27, SD = .930), indicate the same tendency for values as measured by frequencies. Hence, there are substantial differences of problem-based teaching (fully support: 46.9%; a little support: -53.7%; undecided: 6.9%) between the students to whom problem-based teaching is used compared to the students to whom the traditional approach is used.
Assessment impact’ frequencies indicates that most of the respondents (73.3%) of experimental groups and 66.9% of control groups have fully learned from mistakes and study better next time if they don’t do well in an exam; 6.7% of the experimental groups and 6.6% of the control groups have a little learning; meanwhile 20.0% of the experimental groups and 26.4% of the control groups are undecided. Central tendency values for experimental groups (M= 2.73, SD = .774), as well as for control groups (M= 3.40, SD = 1.311), indicate the same tendency for values as measured by frequencies. Therefore, there are small differences of assessment impact (fully learned: 6.4%; a little learned: 0.1%; undecided: -6.4%) between the experimental and control groups of students.
Basic-learning skill’ frequencies indicates that most of the respondents (53.4%) of experimental groups and 53.7% of control groups are fully able to learn the basic concepts taught in the math course; 6.7% of the experimental groups and 6.6% of the control groups are a little able; meanwhile 26.7% of the experimental groups and 26.4% of the control groups are undecided. Central tendency values for experimental groups (M= 4.00, SD = .897), as well as for control groups (M= 3.80, SD = .832), indicate the same tendency for values as measured by frequencies. Hence, there are small differences of basic-learning skill (fully able: -0.3%; a little able: 0.1%; undecided: 0.3%) between the students to whom problem-based teaching is used compared to the students to whom the traditional approach is used.
Basic-learning skill |
Prior knowledge |
Comprehensive learning approach |
Problem-based teaching |
Assessment impact |
|
---|---|---|---|---|---|
Basic-learning skill |
1.000 |
-.008 |
.417 |
.319 |
.029 |
Prior knowledge |
-.008 |
1.000 |
|||
Comprehensive learning approach |
.417 |
1.000 |
|||
Problem-based teaching |
.319 |
1.000 |
|||
Assessment impact |
.029 |
1.000 |
Basic-learning skill |
Prior knowledge |
Comprehensive learning approach |
Problem-based teaching |
Assessment impact |
|
---|---|---|---|---|---|
Basic-learning skill |
1.000 |
.003 |
.443 |
-.048 |
.106 |
Prior knowledge |
.003 |
1.000 |
|||
Comprehensive learning approach |
.443 |
1.000 |
|||
Problem-based teaching |
-.048 |
1.000 |
|||
Assessment impact |
.106 |
1.000 |
H # 1: As shown in
H # 2: As shown in
H # 3: As shown in
H # 4: As shown in
Model Summary |
||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Experimental groupsb |
||||||||||
Change Statistics |
||||||||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
R Square Change |
F Change |
df1 |
df2 |
Sig. F Change |
Durbin-Watson |
1 |
.713a |
.508 |
.493 |
.93370 |
.508 |
33.570 |
4 |
130 |
.000 |
1.802 |
Control groupsb |
||||||||||
Change Statistics |
||||||||||
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
R Square Change |
F Change |
df1 |
df2 |
Sig. F Change |
Durbin-Watson |
1 |
.748a |
.559 |
.544 |
.79902 |
.559 |
36.786 |
4 |
116 |
.000 |
1.768 |
H # 5: As shown in
Coefficients Experimental Groups |
|||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Model 1 |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
95% Confidence Interval for B |
Correlations |
Collinearity Statistics |
||||||
B |
Std.Error |
Beta |
Lower Bound |
Upper Bound |
Zero-order |
Partial |
Part |
Tolerance |
VIF |
||||
(Constant) |
1.016 |
.544 |
1.869 |
.064 |
-.060 |
2.092 |
|||||||
Prior Knowledge |
-.261 |
.107 |
.154 |
-2.430 |
.006 |
-.473 |
-.048 |
-.093 |
-.208 |
-.149 |
.943 |
1.061 |
|
Comprehensive learning approach |
1.067 |
.162 |
.658 |
6.571 |
.000 |
.746 |
1.389 |
.646 |
.499 |
.404 |
.377 |
2.651 |
|
Problem-based teaching |
-320 |
.136 |
.217 |
2.351 |
.002 |
.051 |
.590 |
.565 |
.202 |
.145 |
.445 |
2.246 |
|
Assessment impact |
-.460 |
.111 |
-.315 |
-4.153 |
.000 |
-.680 |
-.241 |
.171 |
-.342 |
- |
.657 |
1.523 |
|
|
|||||||||||||
Model 1 |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
95.0% Confidence Interval for B |
Correlations |
Collinearity Statistics |
||||||
B |
Std.Error |
Beta |
Lower Bound |
Upper Bound |
Zero-order |
Partial |
Part |
Tolerance |
VIF |
||||
(Constant) |
-.150 |
.478 |
-.313 |
.755 |
-1.097 |
.798 |
|||||||
Prior Knowledge |
.181 |
.098 |
.115 |
1.852 |
.006 |
-.013 |
.375 |
.055 |
.169 |
.114 |
.982 |
1.018 |
|
Comprehensive learning approach |
.800 |
.083 |
.629 |
9.640 |
.000 |
.635 |
.964 |
.666 |
.667 |
.594 |
.892 |
1.121 |
|
Traditional approach |
-.314 |
.062 |
-.320 |
-5.047 |
.000 |
-.437 |
-.191 |
-.221 |
-.424 |
-.311 |
.943 |
1.060 |
|
Assessment impact |
.276 |
.095 |
.194 |
2.909 |
.004 |
.088 |
.464 |
.326 |
.261 |
.179 |
.852 |
1.174 |
For experimental groups, beta value for prior knowledge is-.154, for comprehensive learning approach, is .658, for problem-based teaching is .217, and for assessment impact is -.315. The largest beta standardized coefficient is .658, which is for comprehensive learning approach.
For control groups, beta value for prior knowledge is.115, for comprehensive learning approach is .629, for the traditional approach is -.320, for assessment impact, is .194. The largest beta standardized coefficient is .629, which is for comprehensive learning approach.
According to frequencies as well as the central tendency values, there are small differences of prior knowledge between the experimental and control groups of students. Thus, the students to whom the problem-based-learning approach is used reported higher levels of prior knowledge compared to the students to whom the traditional approach is used. Comparing prior knowledge from previous academic year with current knowledge scores it is found that there is a difference between experimental and control group of students, especially in fail and outstanding levels achieved in mathematics. Therefore, faculties, as well as mathematics departments might pre-test prior knowledge of students to strengthen their work to increase their academic success in mathematics.
Frequencies as well as the central tendency values indicate that there are small differences of comprehensive learning approach between the students to whom problem-based-learning approach is used compared to the students to whom traditional approach is used. Hence, faculties, as well as mathematics departments might promote the comprehensive learning approach.
Frequencies as well as the central tendency values indicate that there are bigger differences of problem-based teaching between or the students to whom problem-based-learning approach is used compared to the students to whom traditional approach is used. Hence, faculties, as well as mathematics departments might promote and use more problem-based teaching.
According to frequencies as well as the central tendency values there are small differences of assessment impact between the students to whom problem-based-learning approach is used, compared to the students to whom the traditional approach is used.
Therefore, faculties, as well as mathematics departments might use assessment to increase its impact on students' learning to learn from mistakes and study better next time if they don't do well in an exam.
Frequencies, as well as the central tendency values, indicate that there are small differences of basic-learning skill between the students to whom problem-based-learning approach is used, compared to the students to whom traditional approach is used. Hence, faculties, as well as mathematics departments might support the students to be able to learn the basic concepts taught in the math course.
Pearson correlation outputs indicate that there is a negligible correlation between prior knowledge and basic-learning skill variables (r2 = -.008) for experimental groups as well as for control groups (r2 = .003). Furthermore, there is not a statistically significant relationship between prior knowledge and basic-learning skill by students, thus a random chance could explain the result. The value of correlation indicates that other variables might be important in variance prediction of basic-learning skill scores. So, future researchers may do more work to investigate the influence of other variables on basic-learning skill. The result was not consistent with some previously reported works, who argued that prior knowledge influence basic-learning skill scores (
Pearson correlation outputs indicate that there is a significant positive correlation between comprehensive learning approach and basic-learning skill variables (r2 = .417) for the experimental groups as well as for control groups (r2 = .443). Therefore, high scores of comprehensive learning approach are associated with high scores of basic-learning skill. The value of r2 indicates that other variables might be important in variance prediction of basic-learning skill scores. So, future researchers may do more research to investigate the influence of other variables on basic-learning skill. The result was consistent with some previously reported works, who argued that comprehensive learning approach influence basic-learning skill scores (
Pearson correlation outputs indicate that there is a significant positive correlation between problem-based teaching and basic-learning skill variables (r2 = .319) for the experimental groups, meanwhile, there is a low negative correlation between traditional approach and basic-learning skill for the control groups (r2 = -.048). Hence, high scores of problem-based learning teaching’ strategies are associated with high scores of basic-learning skill. Meanwhile, high scores of traditional teaching are associated with low scores of basic-learning skill. The value of correlation indicates that other variables might be important in variance prediction of basic-learning skill scores. So, future researchers may do more work to investigate the influence of other variables on basic-learning skill. The result was consistent with some previously reported works, who argued that problem-based teaching influence basic-learning skill scores (
Pearson correlation outputs indicate that there is a negligible correlation between assessment impact and basic-learning skill variables (r2= .029) for the experimental groups as well as for control groups (r2 = .106). Therefore, it is expected that the scores of assessment impact are not associated with thw scores of basic-learning skill. The value of r2 indicates that other variables might be important in variance prediction of basic-learning skill scores. So, future researchers may do more research to investigate the influence of other variables on basic-learning skill. The result was not consistent with some previously reported works, who argued that assessment impact influence basic-learning skill scores (
Regression outputs indicate that the total variance of basic-learning skill levels explained by prior knowledge, problem-based teaching, comprehensive learning approach and assessment impact (the model) is 50.8% for the experimental groups and 54.4% for the control groups. The model reaches statistical significance (Sig. = .000).
Problem-based teaching beta value in the experimental groups means that 21.7% of the variance on basic-learning skill is explained by problem-based teaching. Hence, problem- based learning or manipulated variable is making a significant contribution to the prediction of basic-learning skill dependent variable in the experimental groups.
Traditional approach beta value in the control groups is negative and means that 32.0% of the variance on basic-learning skill dependent variable is explained by the traditional approach. Therefore, traditional teaching independent non- manipulated variable is making a significant but negative contribution to the prediction of the basic-learning skill dependent variable in the control groups.
Meanwhile, beta values for prior knowledge and assessment impact in the experimental groups are negatives and are explained 15.4% and 31.5% of the variance on the dependent variable. The largest beta standardized coefficient is .658, which is for comprehensive learning approach. This means that this variable makes the strongest unique contribution to explaining the dependent variable.
Beta value in the control groups for prior knowledge explain 11.5%, and beta value for assessment impact is negative and explain 19.4% of the variance on the dependent variable. The largest beta standardized coefficient is .629, which is for comprehensive learning approach. This means that this variable makes the strongest unique contribution to explaining the dependent variable.
Therefore, problem- based teaching is making a significant positive contribution, meanwhile, the traditional approach is making a significant but negative contribution to the prediction of basic-learning skill. The result was consistent with some previously reported works, who argued that higher prior knowledge, comprehensive learning approach, problem-based teaching, and higher assessment impact scores predict higher basic-learning skill scores (
The results of this study supported by other researchers about the basic-learning skill in mathematics have important implications for future research on academic achievements. Such research should investigate various variables and their relation to basic-learning skill. Results of this study about basic-learning skill also have important implications for practice. The important programs or other interventions should be designed to develop and to support students to obtain better results in mathematics.
Several limitations of the study should be acknowledged as part of the conclusion. First, the measurement of prior knowledge, comprehensive learning approach, problem-based teaching, assessment impact, and basic-learning skill variables is made through using of self- reported instrument. Second, the study included four independent variables, since it is known that basic-learning skill is influenced by other variables as well. The prior assumption was that prior knowledge, comprehensive learning approach, problem-based teaching, and assessment impact student's basic-learning skill.
The results showed that there are small differences of prior knowledge between the students to whom the problem-based teaching approach is used compared to the students to whom the traditional approach is used. The study confirmed that there are small differences of comprehensive learning approach and assessment impact between the students to whom the problem-based teaching approach is used compared to the students to whom the traditional approach is used. The results showed that there are substantial differences of problem-based teaching between the students to whom problem-based teaching is used compared to the students to whom the traditional approach is used. The results showed that there are small differences of basic-learning skill between the students to whom problem-based teaching is used compared to the students to whom the traditional approach is used.
It is found that there is a negligible correlation between prior knowledge and basic-learning skill, and there is not a statistically significant relationship between them. It is found that a comprehensive learning approach correlate positively with knowledge conceived, meanwhile, assessment almost does not correlate. Thus, higher scores of learning approach are associated with higher scores of basic-learning skill. It is found that problem-based teaching correlates positively with basic-learning skill, meanwhile traditional approach correlates negatively with basic-learning skill Thus, higher scores of problem-based teaching are associated with high scores of basic-learning skill, meanwhile higher scores of traditional approach are associated with lower scores of basic-learning skill.
The study found that 50% of the variance of basic-learning skill levels is explained by prior knowledge, problem-based teaching, comprehensive learning approach, and assessment impact. It is found that problem-based teaching explains 21.7% of the variance of basic-learning skill, prior knowledge 15.4%, comprehensive learning approach 65.8%, and assessment impact 31.5%. The other variance may be explained by hidden or unknown variables. The study confirmed that the comprehensive learning approach makes the strongest unique contribution to explaining basic-learning skill in mathematics. Problem-based teaching is making a significant positive contribution, meanwhile, the traditional approach is making a significant but negative contribution to the prediction of basic-learning skill in mathematics.
https://zenodo.org/record/4244124#.X6voxchKiM8