Model Jigsaw Dalam Perkuliahan Pengantar Ilmu Ekonomi Untuk Meningkatkan Kemandirian Dan Prestasi Mahasiswa

The aim of this research is to apply the Jigsaw\u27s model on subject PengantarIlmu Ekonomi at Economic departement of FKlP in Sebelas Maret University to increaseindependent learning and stu-dent achievement. Method used in this research isClassroom Action Research (CAR). According to CAR principles there are research cycles,where the number of research cycles depend on efficacy indicator achieved. In theresearch, the research cycles consist of fours steps, there are: planning, action execution,observation and reflextion. Result describe that learning by using Jigsaw model canimprove student independent learning. By having high independent learning the studentswill be more initiative, exploratary, creative; and have skills to express themself, trying toovercome problem, dare of what they were to be responsible doing; have the ability todescribe the opinion actively in lecturing and looking for experience learning. By havinggood independent learning level, the students can improve the existence of their learningresult. Students try to get information from various source, then to expostulate with otherstudents. These will improve understanding subjects which is inreturn will improve studentlearning achievement

Multivariate analysis for diseased vessels.

<p>Multivariate analysis for diseased vessels.</p

Proposed Path model C (Potential influence of drugs).

<p>The path has estimates of standardized regression weights and estimates of correlations among exogenous variables. The variable (given in parentheses) means “not statistically significant.” HbA1c = hemoglobin A1c; HDL-C = high-density lipoprotein cholesterol; LDL = low-density lipoprotein; MDA-LDL = malondialdehyde-modified LDL; HDL = high-density lipoprotein; HT = hypertension; BMI = body mass index. ACE = angiotensin-converting enzyme; ARB = angiotensin II receptor blocker; CCB = calcium channel blocker.</p

Path model A: Association between relative risk factors.

<p>Path model A: Association between relative risk factors.</p

Proposed Path model B (sub-group analysis of acute coronary syndrome).

<p>The path has estimates of standardized regression weights and estimates of correlations among exogenous variables. The variable (given in parentheses) means “not statistically significant.” UAP = unstable angina pectoris; STEMI = ST segment elevation myocardial infarction; NSTEMI = non-ST segment elevation myocardial infarction; HbA1c = hemoglobin A1c; HDL-C = high-density lipoprotein cholesterol; LDL = low-density lipoprotein; MDA-LDL = malondialdehyde-modified LDL; HDL = high-density lipoprotein; HT = hypertension; BMI = body mass index.</p

Results of path model B1 (Fig 2).

<p>Results of path model B1 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177327#pone.0177327.g002" target="_blank">Fig 2</a>).</p

Results of path model B2 (Fig 2).

<p>Results of path model B2 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177327#pone.0177327.g002" target="_blank">Fig 2</a>).</p

Path model A: Explanatory drawing of possible cascade from BMI to IHD directly and via the low reactivity of BNP, dyslipidemia, hypertension, and HbA1c.

<p>This path has a coefficient showing the standardized coefficient of regressing independent variables on the dependent variable of the relevant path. These variables indicate standardized regression coefficients (direct effect) [simple capitals], squared multiple correlations [narrow italic capitals] and correlations among exogenous variables [capitals inside round brackets]. A two-way arrow between two variables indicates a correlation between those two variables. The total variance in a dependent variable for every regression is theorized to be caused by either independent variables of the model or extraneous variables (e). BMI: body mass index; BNP: B-type natriuretic peptide; e: extraneous variable.</p

Results of path model A (Fig 1).

<p>Results of path model A (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177327#pone.0177327.g001" target="_blank">Fig 1</a>).</p

Multiple logistic regression analysis for IHD.

<p>Multiple logistic regression analysis for IHD.</p