Texas Miracles, [Online] Second Chances, and the Hidden Identity Development Curve

Pennsylvania’s Department of Education Secretary, Dr. Gerald Zahorchak (October 29, 2007), stated “boredom and a lack of challenges” as contributory factors in a student’s decision to drop out of school in Pennsylvania. In addition to the collection of whole group and sub group data, the Pennsylvania Department of Education attempts to collect relevant data to study the reasons for dropping-out. The Pennsylvania Department of Education catalogues the results within a data matrix. The matrix lists six reasons for dropping out (Academic problem, Behavioral problem, Disliked school, Pregnancy/Child care, Wanted to work, and Runaway or expelled) within four program categories (College preparatory, Vocational, Exceptional, and General).  The three most frequent responses in all program categories were disliked school (41%), wanted to work (21%), and academic problem (20%).  As is customary with quantitative studies, the survey did not delineate why students disliked school.  The Silent Epidemic (2007) stated that forty-seven percent of dropouts emanated from a lack of interest in school and the associated learning environment (Paulson, March 3, 2006).  Unfortunately, reporting has flaws because dropouts do not necessarily comply with a school district’s exiting process therefore creating a dilemma and inaccuracies in data collection and reporting.  Data based upon the documented responses provided by students who have conducted official exit interviews with the associated school district do not represent the vast numbers of undocumented dropouts. 

The Texas Miracle and No Child Left Behind

Linda McSpadden McNeil’s 2008 study of 271,000 students from urban districts in Texas indicated that the accountability associated with No Child Left Behind has had a significant impact on the number of dropouts nationwide.  Although the ‘Texas Miracle’ of achievement in the late 1990’s served as the impetus for No Child Left Behind at the federal level, research conducted over the last decade paints a different picture than that which was originally heralded as a great reform model for all to emulate. McNeil states that the pressure on schools to perform at a certain level has affected how schools are managed and how students are taught and serviced.  In essence, McNeil acknowledged that student value is based upon their being an “asset” or “liability” to the school’s success.  Further, No Child Left Behind’s increasingly stringent accountability measures only precipitate principals and administrators to group students by subgroups—identifying low performers, increasing disciplinary action for minor infractions, and beginning the process of moving a student out of school.  Thus, a policy initiative purportedly implemented to bring more equity to education has actually increased the susceptibility of minority subgroups to further adverse institutional practices and policy. 

Studies have shown that positive teacher-student relationships encourage student achievement and satisfaction (Bergin, June, 2009; Baker, 1999; Decker, February, 2007).  This is especially true with an urban at-risk population where secure attachment is contributory when addressing student satisfaction and the effects of a positive, non-threatening learning environment on student achievement (Baker, 1999). Many urban minority at-risk school age students do not possess the internal motivation to succeed.  As a result, students rely on strong relationships with their teachers for leadership and guidance (Marchant, 1990) as early as elementary school. For that reason, supportive and concerned school environments that smooth the progress of student learning and connection contributes to the academic success of at-risk minority school age students (Towns, et al., 2001; Waxman, Huang, & Anderson, 1997).

Push Out

The term ‘push out’ is drawn on to describe the means by which school districts utilize institutionalized practices to move the less desirable student population towards the exit door.  Lehr (2007) states that negative incidences within school leave students with feelings of isolation and discontent.  This pushing out can occur through repeated disciplinary action, standardized teaching methods, teacher attitudes, limited support, and even the pressure of high stakes testing (Oleck, 2008).  A study of the African American and Latino populations in the Denver (CO) Public Schools indicates that minority and low-income students are “taught down to”.  Using the excuse of stressors (adverse status variables) outside the school as a reason to lessen the amount of rigorous schoolwork, school districts ensure African American students enter each school year at an institutional disadvantage (Padres Unidos, 2006). 

Thousands of public school students experience a push out.  To the school district, these students are not representative of the dropout crisis.  School districts tend to rely on graduation rates based upon the number of students graduating in a given school year rather than address the number of students who started ninth grade and subsequently graduated in the traditional four- year cycle.  For example, the National Education Association’s lobbying agenda reported that in 2001, New York City schools graduated 34,000 students while discharging 55,000 high school students.  Although reports indicate that most students were transferring or moving, it is easy to bury thousands of push-outs into the transfer category.  Therefore, true indicators of the dilemma are not available or veiled behind a shroud of secrecy and inaccurate reporting.

Asynchronous Models:  Credit Recovery & Disruptive Innovation

It is safe to say that academic success rests among the intended outcomes of African American students (Lewis & Kim, 2008).  Still, students lacking course credits have few opportunities to accumulate credits in a time-period that will allow for full academic recovery and timely participation in graduation exercises.  Initiatives such as credit recovery programs allow the non-traditional student to realize graduation rather than turn to exiting the realms of academia by dropping out. Although not specific to race and gender, student motivation in e-learning/online programs has been studied (Blanchard & Frasson, 2004; Henry & Stone, 1999) to assess the value of motivational practices in e-learning networks to ensure success and reduce the anxieties of academic pursuits.  Studies on the use of online credit recovery programs report that this type of technological innovation helps currently enrolled students retrieve credits towards graduation and encourage dropouts to return to school (Watson & Gemini, 2008). 

Internal motivation is more easily recognized and acknowledged within an asynchronous credit recovery program due to the self-directed and independent nature of the program.  Because each student participant is engaged within their own specific coursework, they are the only person by which their success or failure is gauged.  Therefore, the independence of the asynchronous credit recovery program places the student participant in the center position.  When student success, motivation, and effort are ostensibly measured against the peer group, students have the potential to pull away from direct interaction due to a heightened level of discomfort within the learning environment. These passive behaviors can ultimately result in increased absences and disengagement from the learning environment altogether. The unfortunate result in too many instances is a student prematurely dropping out of school before realizing graduation. The asynchronous credit recovery program structure can enhance student self esteem and confidence in their pursuits because progress and success are not measured against their peer group or with any degree of subjectivity.  With this, a sense of personal ownership heightens motivation, confidence, engagement, and ultimately, student achievement.

A credit recovery program’s assessments are objective and do not rely on subjective assessments, evaluations, and personal critiques to determine the value of the individual. Further, the self-paced platform of the credit recovery program reduced the day-to-day pressures of performance anxiety (Cavanaugh, 2009). The reduction of performance anxiety increases the capacity of the non-traditional student to advance proficiency in collecting, assembling, and processing information as well as promoting investigative abilities beyond that of academic pursuits. The development of such expands the range by which the non-traditional student establishes resolution skills and negotiates conceptual struggles (Coleman, 2005). To this end, the independent nature of the student to computer interface model places the non-traditional student in a position of constant self-actualization that promotes confidence, growth, and self-esteem while enhancing personal value and identity consideration towards clarification and commitment.

Studies disclose aspects, affects, and characteristics of credit recovery programs that are associated with time schedules, commitment, independence, and isolation from influence that is antithetical to a traditional school model.   Further consideration on the applicability of credit recovery programs on non-traditional student populations beyond those that are credit deficient is necessary to ascertain the various affects and positive behavioral influences such programming has on a non-traditional student population.  It is possible that a wider audience can be served through participation in an on site technology based alternative non-traditional schooling environment (Waters, 2010).

Finally, a technology based asynchronous education model does not threaten the traditional schooling program within a traditional brick and mortar model. Nonetheless, the effects of such programming are undeniably beneficial to a segment of the population that has been confounded with dilemmas directly related to the school environment or external dynamics and complexities unrelated to the school environment.


Baker, J. (1999). Teacher-Student Interaction in Urban At-Risk Classrooms: Differential Behavior, Relationship Quality, and Student Satisfaction with School. The Elementary School Journal. 100(1).  Abstract retrieved from http://www.jstor.org/pss/1002161.

Bergin, C. & Bergin, D. (June, 2009). Attachment in the Classroom. Educational Psychology Review. 21(2).  Abstract retrieved from http://www.springerlink.com/content/m3843268880q0460/.

Blanchard, E., & Frasson, C. (2004). An Autonomy-Oriented System Design for Enhancement of Learner’s Motivation in E-learning. Retrieved www.iro.umontreal.ca/labs/HERON/art/blanchard_frasson_2004.pdf.

Cavanaugh, C. (May 18, 2009). Getting Students More Learning Time Online: Distance Education in Support of Expanded Learning Time in K-12 Schools. Retrieved on from http://www.americanprogress.org/issues/2009/05/distance_learning.html.

Coleman, S. (2005). Why Do Students Like Online Learning? Retrieved from http://www.worldwidelearn.com/education-articles/benefits-of-online-learning.htm.

Decker, D., Dona, D, & Christenson, S. (February, 2007).  Behaviorally At-Risk African American Students: The Importance of Student-Teacher Relationships for Student Outcomes. Journal of School Psychology. 45(1), 83. Abstract retrieved from http://www.eric.ed.gov/ERICWebPortal/custom/portlets/recordDetails/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=EJ748944&ERICExtSearch_SearchType_0=no&accno=EJ748944.

Kim, M. (October 1, 2008). Women of Color: The Persistent Double Jeopardy of Race and Gender.  The American Prospect. Retrieved from http://www.accessmylibrary.com/coms2/summary_0286-37297262_ITM

Lehr, C.A., Johnson, D. R., Bremer, C., Cosio, A. (2007). What Do We Know About Who Drops Out and Why?  Retrieved from http://www.adlit.org/article/20795.

Lewis, M. and Lockheed, M. (2006). Inexcusable Absence:  Why 60 Million Girls Still Aren’t In School and What To Do About It. Washington, DC: Center for Global Development. Retrieved from www.cgdev.org/doc/books/Inexcusable%20Absence/Chapter%202.pdf .

Marchent, G. (April, 1990). Intrinsic Motivation, Self-Perception, and their effects on Black Urban Elementary Students. Paper Retrieved from http://www.eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/20/69/a5.pdf

McNeil, L. M., Coppola, E., Radigan, J., & Vasquez Heilig, J. (2008). Avoidable losses: High-stakes accountability and the dropout crisis. Education Policy Analysis Archives, 16(3). Retrieved from http://epaa.asu.edu/epaa/v16n3/.

Oleck, J. (February 20, 2008). NCLB’s Accountability Feeds Drop-Out Rates. School Library Journal (online).  Retrieved from http://www.schoollibraryjournal.com/article/CA6533974.html

Padres Unidos (January 18, 2006). Drop Outs or Push Outs: Students Vote with Their Feet. CJRC Newsletter. 14. Retrieved from http://www.advancementproject.org/pdfs/cjrc/pushouts.pdf

Paulson, A. (March 3, 2006). Dropout Rates High, but Fixes Under Way. The Christian Science Monitor.  Retrieved from http://www.csmonitor.com/2006/0303/p01s02-legn.html.

Towns, D., Cole-Henderson, B., & Serpell, Z. (2001). The Journey to Urban School Success: Going the Extra Mile. The Journal of Negro Education, 70, 4-19.

Watson, J. & Gemini, B. (June, 2008). Using Online Learning for At-Risk Students and Credit Recovery. NACOL/North American Council for Online Learning. Retrieved from www.nacol.org/promisingpractices/NACOL_CreditRecovery_PromisingPractices.pdf.

Waxman, H. C., Huang, S. L., & Anderson, L. (1997). Classroom Process Differences in Inner-city Elementary Schools. Journal of Educational Research, 91, 49-59. Retrieved from http://www.highbeam.com/doc/1G1-55500202.html.

Zahorchak, G. (October 29, 2007). Education Secretary Outlines Pennsylvania’s Efforts to Address Dropout Situation.: Multifaceted Approach Focuses on Prevention, Intervention and Re-engagement. Retrieved from

Explore posts in the same categories: credit recovery, Dropout Prevention, Education, Eric Waters, Online learning, Policy, Technology integration

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One Comment on “Texas Miracles, [Online] Second Chances, and the Hidden Identity Development Curve”

  1. Doug Bruno Says:

    Good work Eric. It is good to see detailed look at the dropout problem and even better to see someone looking at the use of technology but with a human/relationship component that gets seriously underestimated. Youth respond to positive relationships, they can do the work if presented effectively with technology but the human mentoring and motivation aspect they get from a dedicated person is hard to measure. I think you are on to the reason why (and it is unstudied) many at-risk students don’t succeed in pure cyber environments.

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