The Veritas Blog

How to get the most out of tutoring

Andrew — Wed, 02 Nov 2011 15:34:56 +0000

Tutoring is as old as education itself. Since antiquity students have benefitted from the value of one-on-one academic mentorship. With the growing academic achievement gap, tutoring has gone from a luxury service to standard practice for most every subject, standardized test, or school application. But how do you know if you are getting the most out of your tutor? With the following pointers from Veritas Tutors, you can dismiss any doubt and focus on your academic success.

First, get down to business. A good rapport is essential for any learning relationship, but be careful about becoming too friendly with your tutor. Your time and money are valuable, and you should not waste either. To start on the right foot, you should share a brief agenda (or syllabus) with your tutor before getting started. With that information, your tutor can prepare for your specific needs while you collect the questions and trouble areas in the material you are studying. In education, a little preparation can go a long way.

Prepare for the confusion of today (and tomorrow). While any good tutor can react to your confusion, some students do develop a dependence on tutoring lessons. Some students might even stop paying attention in class. Whereas good tutors react to questions and confusion, the best tutors tutors – like those at Veritas – also prepare students for the confusion of tomorrow. By looking ahead in the syllabus, tutors can best prepare students with short, foundational lessons. As a result, students can follow along and ask (or even answer) questions in class.

Track your progress. As you begin to work with your tutor, do not expect miracles. Like exercise, tutoring can take a while to show its impact. You may not ace you first test or paper, but you will feel a change before you see that boost in your GPA. So try to keep a log of your progress in terms of grades and confidence. Even amid setbacks down the road, you’ll have a visual record of what you have learned and how much you have improved. This is a great way to stay motivated during the course of a long semester or school year.

Deliberately scale back your appointments. Since students often begin tutoring after (rather than before) a setback, students often need to make up lost ground with two or more meetings per week. As students improve, we at Veritas encourage a systematic reduction in tutoring. After several months, you should get the point where you only have occasional check-ins with your tutor for help with important exams or assignments. As tutoring becomes less frequent, it is also essential that students continue with the best practices they have learned from their tutors.

Study on your own (or with a friend). As tutoring becomes less frequent, students should find ways to motivate themselves with short breaks or collaborative learning with friends. The goal is to stay on task as you prepare for each day’s lessons, engage with instructors, review what you learned, and properly study for exams. If you have trouble with your progress, you can always ask your tutor for pointers and occasional lessons to stay on track. Sometimes a study buddy can be a fun and helpful stand-in for a tutor, but be aware that you need to stay on task and not distract each other from the task at hand.

Overall, the best tutors, particularly those at Veritas Tutors, are concerned with each student’s academic success as lifelong learners in and out of the classroom. To achieve this goal requires focused effort, careful management, and an intention of independence for every student. If you follow the advice above, you will not only earn better grades with less stress, but you might even find yourself teaching other students how to achieve the same success you have enjoyed.

Statistics: On Multiple Regression

admin — Tue, 31 May 2011 17:00:10 +0000

For the final installment in this series on introductory statistics for the social sciences, Kyle T. begins discussing multiple regression, a statistical test and topic that can be subject to much more advanced investigation by those students who are interested in learning more about this subject. In short, multiple regression extends regression in a way similar to how factorial AnoVa extends AnoVa; multiple regression allows prediction based on a number of different, independent variables.

We hope that you’ve enjoyed this overview of statistics, geared for social-scientific students and research, this Spring. We’re excited now, leaving Memorial Weekend and entering June, to be transitioning into a new series, geared for premedical students, preparing for the MCAT this Summer. Stay tuned, eager minds; the Veritas video series will return anon!

Warmly yours,

The Veritas Team

On Multiple Regression:

Statistics: On Regression

admin — Tue, 24 May 2011 17:00:21 +0000

As promised, this week Kyle T. addresses regression, a more complex correlation model that does allow for interpretations of causality. However, despite its relative complexity, regression is still just, as Kyle says, “glorified slope-intercept”! Following this logic, you’ll be up and running these analyses in no time.

Hoping you’re enjoying the Spring!

The Veritas Team

On Regression:

Statistics: On Correlation

admin — Tue, 17 May 2011 17:00:05 +0000

Robust like the non-parametric statistics that Kyle T. discussed last week, correlational analyses, Kyle’s topic this week, provide the raw materials for the much vaunted regressive analyses, his topic next week. In essence, as Kyle explains, correlations generally assess how two sets of patterns of data line up next to one another; and, as he emphasizes, however they line up does not say anything about however they may arrive in that relationship: Correlation does not describe causality.

However, the two are related; to learn how correlation and causality do connect, be sure to check back in on our next installment in this series: Regression.

Warmly yours,

The Veritas Team

On Correlation:

Statistics: On Non-Parametric Statistics

admin — Tue, 10 May 2011 17:00:08 +0000

This week Kyle T. addresses the other major type of statistics: non-parametric statistics. As he explains, non-parametrics allow for more robust testing than parametrics, since only non-parametrics can test with non-normal distributions and with ordinal (or less) dependent variables. However, as he adds, non-parametrics’ robustness is counterbalanced by their loss of statistical power; the “collapse” of levels of granularity or fineness within the information gathered about the groups being tested eliminates much of the incisiveness of the tests’ results.

Next up: Correlation!

Warmly yours,

The Veritas Team

On Non-Parametric Statistics:

Statistics: On Factorial AnoVa

admin — Tue, 03 May 2011 16:41:30 +0000

This week Kyle T. expands on the basic AnoVa that he’s presented over the past two weeks and shows how it may be used to test groups that differ one more than one categorical variable. This expansion is called the Factorial AnoVa and can be used in between-subjects, within-subjects, and mixed research designs.

Next week: Non-Parametric Statistics!

Statistically yours,
The Veritas Team

On Factorial AnoVa:

Statistics: On Analysis of Variance (AnoVa – Part Two)

admin — Tue, 26 Apr 2011 17:00:47 +0000

Wrapping up his explanation of the AnoVa, Kyle T. this week presents his third way of understanding the intention of the statistical test by graphically modelling distributions to be compared: By looking at distributions as representations, the comparison becomes clearer than the abstract concepts in consideration.

See you in May!

The Veritas Team

Statistics: On Analysis of Variance (AnoVa – Part One)

admin — Tue, 19 Apr 2011 19:57:14 +0000

This week, as promised, Kyle T. introduces the extension of the t-Test, the Analysis of Variance (AnoVa), meant for comparisons among more than two participant groups. His explanations of the AnoVa tie onto the heart of the test, discerning the variance within groups from the variance between groups; he turns to information theory for its descriptive analogy, educing a signal from noise.

Next week: A brief continuation of explaining the AnoVa, including graphs!

Warmly yours,

The Veritas Team

Statistics: On t-Testing (Part Two)

admin — Tue, 12 Apr 2011 16:00:24 +0000

Continuing last week’s discussion of the t-test, Kyle T. describes the Paired or Dependent Samples t-Test and the ways in which it relates to its already presented sibling, the Independent Samples t-Test. Though this relationship is not sequential as have been the relationships between the other previously presented tests, the two tests nevertheless logically support one another and continue the progression of complexity in statistical analyses that he’s been developing so far.

Next week: AnoVa!

Hoping that you’re enjoying warm weather this Spring,

The Veritas Team

On t-Testing (Part Two):

Statistics: On t-Testing (Part One)

admin — Tue, 05 Apr 2011 16:00:46 +0000

Hey, statistics students and aspiring social scientists; it’s t-Test week!

This week Kyle T. presents the basics of the t-test, the most foundational analysis in inferential statistics. Thus, this test and its consequential relatives will be the analyses that you conduct most often in your own research. Exciting!

Happy April,

The Veritas Team

On t-Testing (Part One):