科学程序技能 Science Process Skills（小学 4,5 ,6年级科学）

科学技能包括科学程序技能［Ｓｃｉｅｎｃｅ　Ｐｒｏｃｅｓｓ　Ｓｋｉｌｌｓ］
和操纵性技能［Ｐｒａｃｔｉｃａｌ　Ｓｋｉｌｌｓ］。　

［Ａ］科学程序技能 Science Process Skills：

１）观察。Observation.

２）试验。Experimenting.

３）分类。Classify.

４）控制变数。Control variables.

• 3 Types of variables [三种变数]：

1. The  independent  variable[操纵性变数］ is the one that you change or select.
2. The  dependent  variable ［反应性变数］ the one changes as a result, and that you measure.
3. The  control  variables［固定性变数］ must not change, so that it is a fair test.

５）精确解释法。Accurate Interpretation.

６）假设。Assumption /hypothesis.

７）运用空间与时间的关系。relationship of space and time.

８）诠释资料。Data Interpretation.

９）沟通。Communication.

１０）预测。Predict.

１１）推断。Infer /educated guess, explanation.

１２）测量和应用数目。Measuring and numbers.

[B] 操纵性技能 Practical Skills：

１）正确地应用与处理科学用具和材料。

２）正确及安全地存放科学用具和材料。

３）正确的方法清洗科学用具。

４）正确及小心地处理标本。

５）准确地画出标本、科学用具和材料。

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Reference参考
[1] Science Processing skill:

［2］:

The Science Process Skills

by Michael J. Padilla, Professor of Science Education, University of Georgia, Athens, GA

Introduction

One of the most important and pervasive goals of schooling is to teach students to think. All school subjects should share in accomplishing this overall goal. Science contributes its unique skills, with its emphasis on hypothesizing, manipulating the physical world and reasoning from data.

The scientific method, scientific thinking and critical thinking have been terms used at various times to describe these science skills. Today the term "science process skills"[科学程序技能］ is commonly used. Popularized by the curriculum project, Science - A Process Approach (SAPA), these skills are defined as a set of broadly transferable abilities, appropriate to many science disciplines and reflective of the behavior of scientists. SAPA grouped process skills into two types-basic and integrated. The basic (simpler) process skills provide a foundation for learning the integrated (more complex) skills. These skills are listed and described below.

Basic Science Process Skills[基本科学程序技能］

Observing［观察］ - using the senses to gather information about an object or event. Example: Describing a pencil as yellow.

Inferring［推断］ - making an "educated guess" about an object or event based on previously gathered data or information. Example: Saying that the person who used a pencil made a lot of mistakes because the eraser was well worn.

Measuring［测量］ - using both standard and nonstandard measures or estimates to describe the dimensions of an object or event. Example: Using a meter stick to measure the length of a table in centimeters.

Communicating［沟通］ - using words or graphic symbols to describe an action, object or event. Example: Describing the change in height of a plant over time in writing or through a graph.

Classifying［分类］ - grouping or ordering objects or events into categories based on properties or criteria. Example: Placing all rocks having certain grain size or hardness into one group.

Predicting［预测］ - stating the outcome of a future event based on a pattern of evidence. Example: Predicting the height of a plant in two weeks time based on a graph of its growth during the previous four weeks.

Integrated Science Process Skills

Controlling variables［控制变数］ - being able to identify variables that can affect an experimental outcome, keeping most constant while manipulating only the independent variable. Example: Realizing through past experiences that amount of light and water need to be controlled when testing to see how the addition of organic matter affects the growth of beans.

Defining operationally - stating how to measure a variable in an experiment. Example: Stating that bean growth will be measured in centimeters per week.

Formulating hypotheses [假设]- stating the expected outcome of an experiment. Hypothesis must be testable. Example: The greater the amount of organic matter added to the soil, the greater the bean growth.

Interpreting data ［诠释数据］- organizing data and drawing conclusions from it. Example: Recording data from the experiment on bean growth in a data table and forming a conclusion which relates trends in the data to variables.

Experimenting［试验］ - being able to conduct an experiment, including asking an appropriate question, stating a hypothesis, identifying and controlling variables, operationally defining those variables, designing a "fair" experiment, conducting the experiment, and interpreting the results of the experiment. Example: The entire process of conducting the experiment on the affect of organic matter on the growth of bean plants.

Formulating models - creating a mental or physical model of a process or event. Examples: The model of how the processes of evaporation and condensation interrelate in the water cycle.

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Ref[3]: Science Process Skills

Website: http://www.longwood.edu/cleanva/images/sec6.processskills.pdf

What Are the Science Process Skills?

 Learning science means more than scientific knowledge. There are three dimensions of science that are all important. The first of these is the content of science, the basic concepts, and our scientific knowledge. This is the dimension of science that most people first think about, and it is certainly very important.

The other two important dimensions of science in addition to science knowledge are processes of doing science and scientific attitudes. The processes of doing science are the Science Process Skills that scientists use in the process of doing science. Since science is about asking questions and finding answers to questions, these are actually the same skills that we all use in our daily lives as we try to figure out everyday questions.

The third dimension of science focuses on the characteristic attitudes towards science. These include such things as being curious and imaginative, as well as being enthusiastic about asking questions and solving problems. Another desirable scientific attitude is a respect for the methods and values of science. These scientific methods and values include seeking to answer questions using some kind of evidence, recognizing the importance of rechecking data, and understanding that scientific knowledge and theories change over time as more information is gathered.

SIX BASIC PROCESS SKILLS

The science process skills form the foundation for scientific methods. There are six basic science process skills:

• Observation

• Communication

• Measurement

• Classification

• Inference

• Prediction

These basic skills are integrated together when scientists design and carry out experiments or in everyday life when we all carry out fair test experiments. All the six basic skills are important individually as well as when they are integrated together.

SCIENCE BEGINS WITH OBSERVATION:

Observing is the fundamental science process skill. We observe objects and events using all our five senses, and this is how we learn about the world around us. 

The simplest observations, made using only the senses, are qualitative observations. For example, the leaf is light green in color, or the leaf is waxy and smooth. Observations that involve a number or quantity are quantitative observations. For example, the mass of one leaf is five grams or the leaves are clustered in groups of five. Quantitative observations give more precise information than our senses alone.

OBSERVATION AND COMMUNICATION

GO HAND IN HAND:

As implied already, communication, the second of the basic science process skills, goes hand in hand with observation. Students have to communicate in order to share their observations with someone else, and the communication must be clear and effective if the other person is to understand the information.

One of the keys to communicating effectively is to use so-called referents, references to items that the other person is already familiar with. For example, we often describe colors using referents. We might say sky blue, grass green, or lemon yellow to describe particular shades of blue, green, or yellow. The idea is to communicate using descriptive words for which both people share a common understanding. Without referents, we open the door to misunderstandings. If we just say hot or rough, for example, our audience might have a different idea of how hot or how rough. If a student is trying to describe the size of a maize(corn) they might use the size of his or her shoe as a referent. The maize(corn) could be either larger or smaller than his shoe.

The additional science process skill of measuring is really just a special case of observing and communicating. When we measure some property, we compare the property to a defined referent called a unit. A measurement statement contains two parts, a number to tell us how much or how many, and a name for the unit to tell us how much of what. The use of the number makes a measurement a quantitative observation.

Students can communicate their observations verbally, in writing, or by drawing pictures. Other methods of communication that are often used in science include graphs, charts, maps, diagrams, and visual demonstrations.

CLASSIFYING INTO GROUPS:

Students are expected to be able to sort objects or phenomena into groups based on their observations. Grouping objects or events is a way of imposing order based on similarities, differences, and interrelationships. This is an important step towards a better understanding of the different objects and events in the world.

There are several different methods of classification. Perhaps the simplest method is serial ordering. Objects are placed into rank order based on some property. For example, students can be serial ordered according to height, or different breakfast cereals can be serial ordered according to number of calories per serving.

Two other methods of classification are binary classification and multistage classification. In a binary classification system, a set of objects is simply divided into two subsets. This is usually done on the basis of whether each object has or does not have a particular property. For example, animals can be classified into two groups: those with backbones and those without backbones.

A multi-stage classification is constructed by performing consecutive binary classifications on a set of objects and then on each of the ensuing subsets. The result is a classification system consisting of layers or stages. A multi-stage classification is complete when each of the objects in the original set has been separated into a category by itself. The familiar classifications of the animal and plant kingdoms are examples of multi-stage classifications.

MAKING INFERENCES AND PREDICTIONS:

Unlike observations, which are direct evidence gathered about an object, inferences are explanations or interpretations that follow from the observations (Inference is educated explanation / interpretation for the observations). For example, it is an observation to say an insect released a dark, sticky liquid from its mouth, and it is an inference to state, the insect released a dark, sticky liquid from its mouth because it is upset and trying to defend itself. 

When we are able to make inferences, and interpret and explain events around us, we have a better appreciation of the environment around us. Scientists’ hypotheses about why events happen as they do are based on inferences regarding investigations.

Students need to understand the difference between observations and inferences. They need to be able to differentiate for themselves the evidence they gather about the world as observations and the interpretations or inferences they make based on the observations. Note that inferences link what has been observed together with what is already known from previous experiences. We use our past experiences to help us interpret our observations.

Often many different inferences can be made based on the same observations. Our inferences also may change as we make additional observations. We are generally more confident about our inferences when our observations fit well with our past experiences. We are also more confident about our inferences as we gather more and more supporting evidence. 

When students are trying to make inferences, they will often need to go back and make additional observations in order to become more confident in their inferences. For example, seeing an insect release a dark, sticky liquid many times whenever it is picked up and held tightly will increase our confidence that it does this because it is up-set and trying to defend itself.

Sometimes making additional observations will reinforce our inferences, but sometimes additional information will cause us to modify or even reject earlier inferences. In science, inferences about how things work are continually constructed, modified, and even rejected based on new observations.

Making predictions is making educated guesses about the outcomes of future events. We are forecasting future observations. The ability to make predictions about future events allows us to successfully interact with the environment around us. 

Prediction is based on both good observation and inferences made about observed events. Like inferences, predictions are based on both what we observe, and also our past experiences. Predictions based on our inferences about events give us a way to test those inferences (hypothesis). If the prediction turns out to be correct, then we have greater confidence in our inference/hypothesis. This is the basis of the scientific process used by scientists who are asking and answering questions by integrating together the six basic science process skills.

In summary, successfully integrating the science process skills with classroom lessons and field investigations will make the learning experiences richer and more meaningful for students. Students will be learning the skills of science as well as science content. The students will be actively engaged with the science they are learning and thus reach a deeper understanding of the content.This will likely lead students to become more interested and have more positive attitudes towards science.

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Note:

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SCIENCE PROCESS SKILLS
SKILL	DESCRIPTION
Observation & communication	Determining the properties of an object or event by using our 5 senses. Observations maybe qualitative(descriptive, such as black, yellow, red hair);  or quantitative (number is involved. e.g. number of legs in a spider; weights of 3 students...).
	Measurement: For quantitative observations: measuring / using numbers. Skills include: Describing quantitatively using appropriate units of measurement Estimating Recording quantitative data Space or time relationships
Classifying:	Grouping objects or events according to their properties. (similarities, differences or interrelations) This maybe serial ordering, binary classification or multi-stage classification.
Inferring	Inference is educated explanation / interpretation for the observations obtained.
Predicting	Forecasting the outcome of a specific future event based on inferences / hypothesis ( An educated guess.)
Making Hypotheses	Proposing an explanation for the observations obtained; based on experience or knowledge that can be tested in the experiment. A hypothesis is testable. [If…..then….. because…..]e.g. If  we break ice into smaller pieces, then it will melt faster, as its surface area exposed to the environment is increased.
Experimenting	1)    Planning & Designing: Designing an investigation that includes procedures to collect reliable data. Planning includes devising a way to test a hypothesis. NOTE: Planning is not always formal. 2)  Identifying the variables: Independent variable(manipulated), dependent variable, and constants. 3)          Interpreting: Considering evidence, evaluating, and drawing a conclusion by assessing the data: In other words, answering the question,”What do your findings tell you?” Finding a pattern, or meaning in a collection of data.
Conclusion and discussion	The hypothesis is supported or not supported.