Critical Analysis of Science Education in Policy Perspective of Pakistan

Worldwide developments in the sciences have changed human lives radically. Science education has an important role in enabling school learners to understand something of what the sciences have found and how the findings were made, along with an awareness of the place of the sciences in any modern society. The objectives of this analytical document study were to review and draw from a wide range of national education polices reports of commissions and conferences and to explore and summarize the issues that need to be addressed, since independence of Pakistan in 1947. These policy documents and reports identify a lack of progress and other studies suggest why. In the light of the findings as well as studies set in other countries, it is suggested that a coherent approach is needed where the curricula in the sciences, the resources to be made available and the assessment systems to be employed are all focusing on the wider aims of science education. In this, there can be a move away for the dominance of memorization and recall towards wider educational goals. The training of science teachers also needs major overhaul. In this way the evidence suggests that a rich and effective provision in science education can be developed and implemented.

Science on Demand

Characterizing science as a public good, as Steve Fuller notes, is a part of an ideological construal of science, linked to a particular portrayal of science in the postwar era that was designed to provide a rationale for the funding of pure or basic science. The image of science depended on the idea of scientists as autonomous truth-seekers. But the funding system, and other hierarchies, effectively eliminated this autonomy, and bound scientists tightly to a competitive system in which the opportunity to pursue ideas in science depended on peer approval in advance. Funding agencies then turned to assessments of impact. John Ziman had already recognized the effects of these changes in the nature of science, and characterized it as “reliable knowledge” produced on demand from funders. As the competition for funds increased, there were further changes in the nature of science itself toward “reliable enough” knowledge. This made science into a “good”. but a good in the sense of results produced for funders, a transformation that left the original epistemic aims of science behind.

Uncertainty in Scientific Explanations

One of the chief aims of science is understanding. The primary way that we achieve understanding of natural phenomena is by constructing explanations of how and why the phenomena occur as they do. The explanations provided by science are inherently uncertain. Due to the complexity of the phenomena being explained and our limitations as humans, scientists rely on models when constructing explanations. By their vary nature, models are uncertain because they essentially involve idealizations (abstractions or distortions of the facts) for the purpose of simplification. Although scientists legitimately infer that the best explanation of a given phenomenon is true, this method of inference is always uncertain for at least two reasons. The first is simply that the data being explained are limited (i.e., there is always more data that could have been gathered). The second is that there are always alternative explanations that might later be discovered.

Physics Experiments Planned by The Students Themselves - Higher Secondary Education

More than 15 years ago we started to implement in our physics curriculum for 17 years old pupils physics experiments planned by students themselves. Each student must learn, how to prepare and perform physics experiment. The leading idea of this endeavor is “student must do, what she/he wants, at least sometimes”. As a most problematic part of this task is, as has been proved, to teach students to formulate a problem - a question, which can be answered by an experiment and also to formulate a hypothesis, a prediction based on the previous knowledge or based on the information gathered from secondary sources. As important we also see the connection of planning experiments to the goals and aims of science education and sensibility of it from the view of pupils and their parents. Planning experiments by students themselves is a task involving a manifold cluster of means of knowledge gathering and utilization. As generally in creativity, the crucial role has memory. The student applies his/her knowledge. But, at the same time, he/she learns, what is the optimal, useful strategy and structure of working, optimal management for a teamwork. Within planning, a student flips through external sources of information, usually, electronic sources or textbooks, focus his/her attention to information interesting or potentially useful for the phenomenon examined by the experiment just planned. Student remembers, what equipment is available, looks for other equipment and material. Of course, the student also learns to write scientifically, to write in a manner, that nothing hampers understanding of the focus, process, and outcomes. Part of the article is devoted to the topic of development abilities of pre-service physics teacher‘s to scaffold the process of planning experiments of their future students.

Estimating Causal Effects from Nonparanormal Observational Data

One of the basic aims of science is to unravel the chain of cause and effect of particular systems. Especially for large systems, this can be a daunting task. Detailed interventional and randomized data sampling approaches can be used to resolve the causality question, but for many systems, such interventions are impossible or too costly to obtain. Recently, Maathuis et al. (2010), following ideas from Spirtes et al. (2000), introduced a framework to estimate causal effects in large scale Gaussian systems. By describing the causal network as a directed acyclic graph it is a possible to estimate a class of Markov equivalent systems that describe the underlying causal interactions consistently, even for non-Gaussian systems. In these systems, causal effects stop being linear and cannot be described any more by a single coefficient. In this paper, we derive the general functional form of a causal effect in a large subclass of non-Gaussian distributions, called the non-paranormal. We also derive a convenient approximation, which can be used effectively in estimation. We show that the estimate is consistent under certain conditions and we apply the method to an observational gene expression dataset of the Arabidopsis thaliana circadian clock system.