Monday, January 30, 2017

Petrosino, A. J. and Mann, M. J. (2017) Pre-Service Teacher's Knowledge

The following is some work I have been working on for the past number of months. The initial work (poster session) for this will be presented at a conference in Belton, TX in March-- a more extensive and detailed presentation will be made at the American Educational Research Association Meeting in April and -- a draft for publication should be completed somewhere between those who meetings. The results are a little surprising and should be of interest to a number of policy makers, teacher educators, and educational psychologists. We are currently collecting more data this semester which will not be included in the previously mentioned work but will hopefully help us understand this phenomena with a little more clarity. -Dr. Petrosino 

Petrosino, A. and M. J. Mann (2017). Pre-Service Teacher's Knowledge. 

ABSTRACT: This research evaluates the science content knowledge of 133 pre-service teachers with a high disciplinary content background and good pedagogy background (HDB), 121 pre-service teachers with a typical college disciplinary background and high pedagogy background (TDB) and a subpopulation of the university population of 100 students with typical college disciplinary background and no pedagogical background (TDPB). The participants took a survey of released science Praxis questions. A pairwise comparisons were performed using Dunn's (1964) procedure with a Bonferroni correction for multiple comparisons and an effect size was determined. Earth science questions revealed statistically significant differences between the TDB(Mdn=24.00) and HDB(Mdn=25.00) (p=.017) but not between the TDPB(Mdn=25.00) or any TDPB group combination. Science processes questions revealed statistically significant differences between the groups TDB(Mdn=15) and HDB(Mdn=17) (p=.002) and between TDPB(Mdn=15) and HDB(Mdn=17) (p=.000) but not between the TDPB(Mdn=15) and HDB(Mdn=15). Life sciences questions revealed statistically significant differences in groups between the TDB(Mdn=22) and HDB(Mdn=26) (p=.000) and between TDPB(Mdn=24) and HDB(Mdn=26) (p=.000) but not between the TDPB(Mdn=24) and TDB(Mdn=22) and physical science questions revealed statistically significant differences in groups between the TDB(Mdn=33) and HDB (Mdn=35) (p=.000) and between TDPB(Mdn=30) and HDB(Mdn=35) (p=.000) but not between the TDPB(Mdn=30) and TDB(Mdn=33). Students from a program which emphasized high disciplinary knowledge scored best on content and pedagogy questions. Students with no STEM or education major scored as well and sometimes better than the group of students with typical disciplinary content and high pedagogical content knowledge and less than the high disciplinary knowledge students.

Friday, January 27, 2017

International Holocaust Remembrance Day 2016

On this International Holocaust Remembrance Day, I find myself reflecting on efforts the Hoboken Curriculum Committee and I put toward a full curriculum to help educate the children of the Hoboken Public Schools about the Holocaust (Link 1, Link 2, Link 3, Link 4, Link 5, Link 6, Link 7). This effort included coordination and communication with the NJ Commission on Holocaust Education. A wonderful group of committed educators dedicated to informing the children of the state about the Holocaust and the atrocities of genocide, prejudice, and bulling.

The core mission of the New Jersey Commission on Holocaust Education is to promote Holocaust education in the State of New Jersey. On a continual basis, the Commission shall survey the status of Holocaust/Genocide Education; design, encourage and promote the implementation of Holocaust and genocide education and awareness; provide programs in New Jersey; and coordinate designated events that will provide appropriate memorialization of the Holocaust on a regular basis throughout the state. The Commission will provide assistance and advice to the public and private schools and will meet with county and local school officials, and other interested public and private organizations, to assist with the study of the Holocaust and genocide.

For more information on the NJ Commission on Holocaust Education please: CLICK HERE  

Yad Vashem- The World Holocaust Remembrance Center

Sunday, January 22, 2017

Under Kids First and Kids First legacy Majority Leadership- Hoboken High School Graduation Rate Falls Below NJ State Average for 6th Consecutive Year- 2016 Graduation Rates for All Hudson County's Public High Schools

For the sixth consecutive year the Hoboken Public Schools under the Kids First and Kids First legacy Board leadership (i.e. "Forward Together") produced a high graduation rate below the New Jersey state average.

NJ.COM is reporting that three Hudson County public high schools -- McNair Academic, Infinity Institute and Liberty -- had perfect graduation rates last year, according to data released on January 12, 2017 by the NJ state Department of Education (DOE).

Research shows that low graduation rates correlate with dropping out of high school which has documented impacts on income, incarceration, single motherhood, and public resources (see IN DEPTH section at end of this article). 
For the sixth consecutive year under Kids First and Kids First legacy district leadership, the Hoboken School District (HHS) recorded a high school graduation rate below the NJ state average. This coincides with concurrent failure over the same time period in the QSAC DPR in Instruction and Program, elevated rates of violence and vandalism compared to county and state averages, multiple school and district configurations, and very low scores on the PARCC exams. Further analysis of the 2016 High School graduation data (see Figure 1) indicates an 83.3 graduation rate for economically disadvantaged students at Hoboken High School .

Figure 1: 2016 Hoboken High School Graduation Rates-Dissagregated
NJDOE website for High School Graduation Rate: CLICK HERE 
The state average graduation rate is 90.1 percent, up from 89.7 last year, NJDOE officials said.
Want to know more about the impact of dropping out of high school? Please click here to see 11 interesting facts and things you can do: CLICK HERE
Snyder High, which has the lowest graduation rate in the county and saw a dip in graduation success -- from 56 percent in 2015 to 50 percent this past school year, according to the DOE statistics.

Here are the graduation percentage rates for public high schools in Hudson County for the 2015-16 school year:
School                     %
**Academic McNair   100
**Infinity Institute       100
**Liberty                    100
*High Tech                 99
*County Prep             99
Harrison                     95
Secaucus                   94
NJ State Avg             90.1
Kearny                       90
Weehawken               90
Bayonne                    86
Hoboken                   86

Hoboken                   83.33 (economically disadvantaged rate) 
North Bergen             83
Memorial                   83
Union City                 80
**Dickinson               78
Jersey City                75 
**Ferris                     75
**Lincoln                   69
**Snyder                   51
* -- High Tech and County Prep are part of the Hudson County Schools of Technology, which include a number of academies. The graduation rate for the HCST district is 93 percent.
** -- These schools make up the Jersey City district high schools. The graduation rate for the entire district is 75 percent.

Income- Perhaps the most widely discussed consequence of not finishing high school is its impact on income potential. Students who drop out of high school earn significantly less than their peers who graduated from high school.

Incarceration- Dropouts are also more likely to be incarcerated in prison. According to a study by the Center for Labor Market Studies, high school dropouts are more than 63 times more likely to be incarcerated than four-year college graduates and more than six times more likely to be incarcerated than those with only a high school diploma. 

Single Motherhood- Single motherhood is both a cause and a consequence of not finishing high school. Among women aged 16 to 24, high school dropouts were the group most likely to be single mothers, with 22.6 percent of this group being single mothers.

Public Resources- Because high school dropouts earn less income, are more likely to be incarcerated and become single mothers at disproportionate rates, they use more public resources. According to a study by the Alliance for Excellent Education, increasing the male high school completion rate by just 5 percent would save the nation $4.9 billion in crime-related expenses. Likewise, if all students graduated, incomes would increase, and reliance on a program like Medicare would be reduced enough to save the nation $17 billion.

2016 State HS Graduation Rate: 90.1%; Hoboken Graduation Rate: 86.0%
2015 State HS Graduation Rate: 89.67%; Hoboken Graduation Rate: 83.33%
2014 State HS Graduation Rate: 88.6%; Hoboken Graduation Rate: 86.78%
2013 State HS Graduation Rate: 87.5%; Hoboken Graduation Rate: 85.43%
2012 State HS Graduation Rate: 86.46%; Hoboken Graduation Rate: 74.53%
2011 State HS Graduation Rate: 83.17%; Hoboken Graduation Rate: 81.99%

Tuesday, January 17, 2017

Hoboken Board of Education - January 17, 2017 Detailed Agenda

Wednesday, January 11, 2017

Petrosino and Mann (2017)- Submission Acceptance Notification

1980 Hoboken High School Baseball Team
Its always a great pleasure to present research with senior doctoral students. 

Dear Dr. Anthony Petrosino and Michele Mann.

Congratulations on the acceptance of your abstract titled Pre-Service Teacher's Knowledge to the Science Education strand of the Texas Academy of Science! While the meeting is still a couple of months away, I have listed a few things you can do to help our sessions run more smoothly.
  1. All presentations for each session need to be loaded on the computer before that session begins. There should be time before the first session and during the breaks
    1. Name your presentation file with YOUR last name (naming your file TAS2011 does not tell me whose file it is).
    2. Please bring your presentation on a flash drive.
    3. IF you don’t bring your presentation to be loaded ahead of time, we will try to load it when it is time for you to begin speaking; HOWEVER, your presentation will still have to end at your regularly scheduled time.
  2. A new talk is scheduled to begin every 15 minutes. In order to keep the session on schedule the moderator will signal you (by waving or by standing up temporarily) when 11minutes have passed. At 13 minutes the moderator will stand up and continue standing. You need wrap up quickly at this point because at 14 minutes the moderator will move to the front of the room to get the next presenter’s presentation opened on the computer and allow for audience members to change rooms if they need to.
  3. After each presentation, themoderator will determine if enough time is available for the audience to ask questions. (It is better to end your presentation with a Thank You rather than asking for questions since it is not up to you do determine if there is time for questions. This also allows for applause by the audience before themoderator calls for questions.) The moderator will stop the questions when it is time to transition to the next speaker. People with further interest in your work are welcome to talk with you at the next break.
  4. If you are in competition for a student award, remember that one component being evaluated is how you respond to questions. If you don’t leave time for questions, the judges will not have the opportunity to see how well you respond to questions.

Thank you in advance for your cooperation. I look forward to meeting you in person at Mary Hardin-Baylor!



Tuesday, January 3, 2017

Hoboken Board of Education- Detailed Agenda January 3, 2017

Monday, January 2, 2017

Petrosino and Mann (2017): "Data Modeling for Pre-Service Teachers and Everyone Else"- Journal of College Science Teaching

Dear Dr. Petrosino:

Your manuscript entitled "Data Modeling for Pre-Service Teachers and Everyone Else" has been successfully submitted online and is presently being given full consideration for publication in the Journal of College Science Teaching.

Your manuscript ID is xxx-Oct-Jxxx-F-1101.xx.

Please mention the above manuscript ID in all future correspondence or when calling the office for questions. If there are any changes in your street address or e-mail address, please log in to Manuscript Central and edit your user information as appropriate.

You can also view the status of your manuscript at any time by checking your Author Center.

Thank you for submitting your manuscript to the Journal of College Science Teaching.

Journal of College Science Teaching Editorial Office

Summary of PaperThis research was aimed at developing teacher thinking about data modeling. Data modeling is a description of the way data is organized. In the past, teachers were expected to teach science from the ideas of science–as-reasoning and science-as-conceptual-change however with the introduction of NGSS, teachers now need to incorporate science-as-practice.   NGSS integrates eight practices of science, which students are to use from kindergarten onward. These practices are asking questions, developing and using models, designing and conducting investigations, using data, using computational thinking, constructing explanations, using argument from evidence and obtaining, evaluating and communicating information. All of these practices are part of the curriculum the preservice teachers enacted during this research.  Future teachers need to have opportunities to use these practices.  Without a solid understanding about how to gather data, and the variability of data it is not possible to correctly interpret data or correctly use arguments supported by data. These skills are needed by all K-16 students including preservice teacher educators.  Statistics are ubiquitous and we all need to understand what the data mean. We have also used this same curriculum with science and engineering majors to help them better understand data modeling. Our position is that data modeling is a process that all K-16 students need to understand.