Course-Taking Patterns of Community College Students Beginning in STEM: Using Data Mining Techniques to Reveal Viable STEM Transfer Pathways

2015 ◽  
Vol 57 (5) ◽  
pp. 544-569 ◽  
Author(s):  
Xueli Wang
Keyword(s):  
College Students ◽  
Data Mining ◽  
Community College ◽  
Community College Students ◽  
Data Mining Techniques ◽  
Using Data

Impact of Community College Student Debt Levels on Credit Accumulation

2017 ◽  
Vol 671 (1) ◽  
pp. 132-153 ◽  
Author(s):  
Dominique J. Baker ◽  
William R. Doyle
Keyword(s):  
College Students ◽  
Community College ◽  
Community College Students ◽  
Instrumental Variables ◽  
Community College Student ◽  
Student Debt ◽  
Academic Credit ◽  
Credit Hours ◽  
Using Data ◽  
The Impact

Most community college students do not borrow to pay for their education. However, in recent years more students are borrowing and, when they borrow, accumulating large amounts of debt. To help clarify whether increased debt burdens are aiding community college students or harming them, we explore the impact of borrowing on academic credit hour accumulation. Using data from the Education Longitudinal Study 2002–2012, we provide multiple estimates of the impact of borrowing on credit hour attainment among community college students. Standard estimates suggest that community college students who borrow complete fewer credit hours than students who do not borrow, although the influence is relatively small (about two credits two years after enrollment). Instrumental variables estimates suggest that the impact of borrowing on credits attained is not significant two years after enrollment but is substantial eight years after enrollment (allowing students multiple enrollment spells).

Assessing the Efficacy of an Online Stress Management Intervention for Community College Students

2014 ◽  
Author(s):  
Liza N. Meredith ◽  
Patricia A. Frazier ◽  
Jacob A. Paulsen ◽  
Christiaan S. Greer ◽  
Kelli G. Howard ◽  
...  
Keyword(s):  
College Students ◽  
Community College ◽  
Community College Students ◽  
Stress Management ◽  
Management Intervention ◽  
Stress Management Intervention

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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