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Article Excerpt
This research aims to devise a set of computer-based tools to meet the diverse needs of learners for comprehending a science learning unit, namely work. A model of computer-based tools on the learning unit for developing procedural knowledge for solving work problems was developed together with a set of teacher customization and collaboration tools. The main components, developed and implemented in an integrated manner for both students and teachers, are Student Activity Environment, Curriculum Authoring Center, Global Activity Center, and Teacher Collaboration Tools. The framework of supporting students through teachers' collaborative course authoring, considering the different backgrounds of the students and preferred teaching/learning style of teachers/students, was evaluated with students and teachers using two different task regimes. The evaluation studies presented encouraging and promising results.
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Misconceptions are troubling issues for teachers and students in science. This is especially true in school science and physics due to its often abstract nature. Students arrive in the science classroom with preconceptions that are often contradictory to accepted science thinking. These naive theories may lead to misconceptions and thus may interfere with accepted concept development (Welmar, 1996). Students may have two distinct perspectives of science: One is reserved for the formal learning setting in the classroom while the other is used outside that setting in everyday life (Cadmus, 1990; Driver, Squires, Rushworth, & Wood-Robinson, 1994). For example, students' misconceptions in work learning unit, that this study focuses, are identified as follows (Amasci, 2004): (a) failing to identify the direction in which a force is acting; (b) believing that any force times any distance is work; (c) believing that machines put out more work than we put it in: Not realizing that machines simply transform the form of the work we do; and (d) believing that the mass effects the work done under every condition.
Visualization of scientific phenomena and laboratory experiences have been important components of the reinforcement and understanding of physics concepts: Visualizing phenomena through demonstrations, simulations, models, real-time graphs, and video can contribute to students' understanding by attaching mental images to these concepts (Escalada, Grabhorn, & Zollman, 1996). These visualization techniques not only allow students to observe how objects behave and interact, but also provide students with visual associations that they may capture, and preserve the essence of physical phenomena more effectively than do verbal descriptions (Cadmus, 1990).
Computer simulations can help students to understand invisible conceptual worlds of science through animation, which can lead to more abstract understanding of scientific concepts (Hwang & Esquembre, 2003). Quantitative data can be manipulated and visualized to help students form a qualitative mental picture. Such complex experiences can help students identify patterns within simulations, and formulate explanations for phenomena in terms of models and theories. Simulations must not only allow learners to construct and manipulate screen "objects" to explore underlying concepts, but they must also provide learners with the observation and manipulation tools necessary for exploring and testing hypotheses in the simulated world (Jonassen, 1996). Combined with graphical representations, simulations should allow the learner to visualize abstract concepts and to link them to prior knowledge, thereby fostering conceptual learning.
These support measures typically guide science learners to (a) focus on particular variables of the underlying model, (b) generate hypotheses about relationships between these variables, (c) conduct simulated experiments to test the hypotheses, and (d) evaluate the hypotheses in the light of the observed results. These tasks are demanding but Chandler (2004) pointed out that interactive activities in science will be useful if they are specifically related to the learning unit and if the knowledge base of the learner is also taken into account. Teachers may customize and relate interactive computer-based activities for the learner by considering the knowledge base of the learner.
A study focusing on students' problems, should also consider the teachers' role in overcoming those problems. Real classroom interaction requires addressing specific learning problems and customization of lessons on an ongoing basis. When teachers are able to design and alter applications, they will then be better able to address learning problems. Many educators (Twining, 1995; Finlayson & Perry, 1995; Resnick, 1993) believe that using computers efficiently in instruction demands knowledge and understanding of hardware and software, philosophical understanding of the nature of subject and pedagogical skills and abilities related to class organization, management and teaching styles. According to Dunlap, Neale, and Carroll (2000; p. 15) teachers need (a) to derive information, monitor the use, and utilize the special resources that computer network technology makes available; (b) to have quick and easy access to the information salient to their planning; (c) notification of salient information and feedback about student progress and problems as well as teaching changes, and (d) ways of managing computer group work that allow for a better understanding of the actions of computer-mediated groups and their instructors and a better ability for teachers to make adjustments. Moreover, planning for remote collaborative work needs to be more flexible and relaxed to allow for instructional differences.
Computerized learning environments should not be built only by expert programmers. Rather, it is necessary to continue developing new types of cognitive environment tools, so that all teachers can participate in the construction of technology rich learning environments. A number of sophisticated tools have emerged for creating interactive multimedia software including commercially available products. The general purpose tools serve a variety of functions; however offer little in the way of design constraints governing the type of software, which can be produced. The result is a tool supporting a broad range of possible applications, but none of which can be created with much guidance from the tool itself. In doing so, they base the interaction around general models of instruction, which unfortunately are too general to serve as a specification for a piece of educational software (Bell, 1999). There is a rough principle that authoring tools tailored for specific tasks or instructional situations can better support the needs of the student and author/teacher for those situations (Murray, Blessing, & Ainsworth, 2003, p. 500). Many researchers (Salomon, 1990; Welch & Brownell, 2000) have pointed out that technology is effective when developers thoughtfully consider the merit and limitations of a particular application and when they employ effective pedagogical practices to achieve a specific objective. Further, Hasebrook and Gremm (1999) argued that learning gains are mainly due to instructional methods and thus many researchers aim at making their tutoring systems more effective using "intelligent" software technologies to adapt to the learners' demands, abilities and knowledge. The same applies to...
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