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critiques of student engagement
The Importance of Student Engagement
The Importance of Student Engagement
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3. DATA
We consider a large set of CLCT student usage data, collected in 2010. Although the tutor was used in several thousand schools across the United States, its full logging capability was activated for only about 20% of schools in which it was used. Our initial dataset covered 144,080 registered students in 899 schools with close to 473 million records overall, including activity unrelated to problem-solving, like signing in, signing out, and solving practice problems. After extracting targeted, substantive, problem-solving activity, we arrived at a dataset that included 342 schools, 72,082 students, and 88.6 million problem-solving actions.
We queried the National Center for Education Statistics (NCES) and internal data for school metadata that included the number of students enrolled (as a proxy of school’s relative size), student-teacher ratio, number of students eligible to receive free or reduced price lunch (as a proxy for socio-economic status), and roughly the setting of the school’s location: rural, suburban, or urban. Although some of the school metadata from NCES and internal records were from the year 2011, we assume that fluctuations in the numbers are negligible for our analyses. We matched full NCES and internal records for a subset of 232 schools, narrowing our selection to, 55,012 students with substantive usage (i.e., attempting one than one unit of instruction) and 67.3 million problem-solving actions.
In addition to the school metadata, we computed student performance statistics from our logs. For each school we have computed the average number of distinct units students were attempting and the standard error of the number of units attempted. To further characterize schools we ran a mixed effects logisti...
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...r Modeling, Adaptation, and Personalization (UMAP 2010), Big Island, HI, USA, 2010 (pp. 255-266). Springer.
[6] Ritter, S., Anderson, J.R., Koedinger, K.R., Corbett, A.T. (2007). Cognitive Tutor: applied research in mathematics education. Psychon Bull Rev, 14:249-255.
[7] Wang, Y. & Beck, J. (2013). Class vs. Student in a Bayesian Network Student Model. In 16th International Conference on Artificial Intelligence in Education (AIED 2013), Memphis, TN, USA, 2013 (pp. 151-160). Springer.
[8] Ward, J. H. (1963) Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58, 236–244.
[9] Yudelson, M., Koedinger, K. R., & Gordon, G. J. (2013). Individualized Bayesian Knowledge Tracing Models. In 16th International Conference on Artificial Intelligence in Education (AIED 2013), Memphis, TN, USA, 2013 (pp. 171-180). Springer.
Van de Walle, J., , F., Karp, K. S., & Bay-Williams, J. M. (2010). Elementary and middle school mathematics, teaching developmentally. (Seventh ed.). New York, NY: Allyn & Bacon.
3. U.S. Department of Education Institute of Education Sciences National Center for Education StatisticsInternational Year of Statistics 2013 (Statistics2013). "Fast Facts." Fast Facts. N.p., n.d. Web. 3 Dec. 2013. .
This program is generally focused to gather statistics on grade school inequalities that effect the entirety of the state. Researchers have suggested that school funding levels and student success rates are largely unrelated. The ability of schools to educate their children is affected solely by the teacher’s ability to teach. Yet for a number of years there has been a steady debate centered around the essentially counter-intuitive idea that the relationship between school resource levels and student performance is the only connection to California’s academic gap/ 1971, the California Supreme Court ruled this system of granted unequal funds to millions of children unconstitutional, promising California’s the state would standardize finances across all school districts. To do this California created Proposition 13 in 1978. This proposition reduced the local property tax revenues available to schools, and the state had to provide even more financial support to maintain similar funding levels across districts. Though it leveled the playing field for the middle class, this tactic created no growth in academic equality. In order to ensure a balance in opportunity to academically excel, California needs to scrutinize the funding system, to utilize all financial resources in ways that
Education is a topic in which there are so many contributing factors and variables that narrowing the field of research is difficult. To begin, demographics of each individual county are essential for understanding the student population from raw data. Statistics such as population, average annual salary, per child expenditures, and financial budgets add some perspective of what counties have in terms of financial resources. Another critical consideration is the proportion of minority students. Non English speaking students and special education require a higher degree of resources and more financial commitment from educational systems. Schools with high minority populations also have lower overall parental incomes which translate into increases in free and reduced lunch and breakfast. Food insecurity, family stressors, unsafe environments, and the lack of support systems are fundamental contributors to children’s educational deficits. Other important factors to consider are the availability of parental involvement, extracurricular activities, and local, state, and federal programs and legislation. In visiting the schools, the focus was on evaluating the individual school facilities and the area in which they are located.
America’s children have found increasing difficulty with school. The curriculum in schools is claiming to be harder in higher levels, but the lack of focus and direction in the younger grades has made for decreased grade levels and lower mastery in several basic areas such as math, writing, and reading skills. Standardized test scores are at an all time low, as increasing amounts of children progress through the educational system having not at...
Rittle-Johnson, B., & Alibali, M. W. (1999). Conceptual and procedural knowledge of mathematics: Does one lead to the other? Journal of Educational Psychology, 91(1), 175-189.
Bennett, J., Briggs, W., & Triola, M. (2014). Statistical reasoning: For everyday life (14th ed.). Boston: Pearson Education, Inc.
Education is an integral part of society, school helps children learn social norms as well as teach them how to be successful adults. The school systems in United States, however are failing their students. In the world as a whole, the United States is quickly falling behind other countries in important math and reading scores. The United States ranked thirtieth in math on a global scale and twentieth in literacy. This is even more true in more urban, lower socio-economic areas in the United States. These schools have lower test scores and high dropout rates. In Trenton Central High School West, there was an 83% proficiency in literacy and only 49% of the students were proficient in math. Many of these students come from minority backgrounds and are often from low income families. There are many issues surrounding these urban schools. There is a severe lack of proper funding in these districts, and much of the money they do receive is sanctioned for non-crucial things. Schools also need a certain level of individualization with their students, and in many urban classes, this simply does not happen. While there are many factors affecting the low performance of urban schools, the lack of proper funding and distribution of funds, the cultural divide between teachers and students in urban districts, along with the lack of individualization in urban classrooms are crucial reasons to explain the poor performance in these districts. Through a process of teacher lead budget committees and further teacher education, urban schools can be transformed and be better equipped to prepare their students for the global stage.
Krause, K, Bochner, S, Duchesne, S & McNaugh, A 2010, Educational Psychology: for learning & teaching, 3rd edn, Cengage Learning Australia, Victoria
Tabula rasa. (2010, March 4). In Wikipedia, The Free Encyclopedia. Retrieved 21:54, March 15, 2010, from http://en.wikipedia.org/w/index.php?title=Tabula_rasa&oldid=347737376
IB Diploma Educators and Students. theory of knowledge.net. 12 january 2014. webpage. 30 april 2014.
Big data is a concept that has been misunderstood therefore I will be writing this paper with the intentions of thoroughly discussing this technological concept and all its dimensions with regard to what constitutes big data and how the term came about. The rapid innovations in Information Technology have brought about the realisation of big data. The concept of big data is complex and has different connotations but I intend to clarify its functions. Big data refers to the concept of a collection of large and complex amounts of data that are found extremely difficult to notate or even process by most on-hand devices and database technologies.
Bercher D A, 2012, Self-Monitoring Tools and Student Academic Success: When Perception Matches Reality, Journal of College Science Teaching 41.5 May/Jun: 26-32.
T. Mitchell, Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Draft Version, 2005 download
Roscoe, J.T. (1975). Fundamental research statistics for the behavioural sciences. 2nd ed. Holt, Rinehart and Winston: Fort Worth