The effects of instructional support and learner interests when learning using computer simulations

Within the scope of this study, the effectiveness of two kinds of instructional support was evaluated with regard to the learner’s interests. Two versions of a simulation program about the respiratory chain were developed, differing only in the kind of tasks provided for instructional support: One version contained problem-solving tasks, the other one contained worked-out examples. The focus was on the learner’s interest in the subject and in computers. The first goal of the study was to find to what extent computer simulations incorporating the different kinds of instructional support have positive effects on situational subject-interest. The second goal was to evaluate the interactions between the learner’s interests and the instructional support with regard to the learning results (subdivided into factual knowledge and understanding). Simulations with worked-out examples were shown to have positive effects on the learner’s situational interest in the subject. This was not found to be the case in simulations with problem-solving tasks. Regardless of the kind of instructional support, learners with little interest in the subject were able to achieve significant gains in factual knowledge. However, improvement in understanding was dependent on the kind of instructional support.     

Computer-supported example-based learning: When instructional explanations reduce self-explanations

We investigated whether the findings from worked-out example research on the effects of self-explanation prompts and on instructional explanations can be generalized to other example types – in this case: solved example problems. Whereas worked-out examples consist of a problem formulation, solution steps, and the final solution, solved example problems merely provide the problem formulation and the solution. We employed a first module of a computer-based environment for student teachers presenting solved example problems from which they learned how to select and to design worked-out examples for high school students. The participants (47 student teachers for German low- and medium-track schools and 33 student teachers for high-track schools) were randomly assigned to the cells of our 2 × 2 design (with vs. without self-explanation prompts; with vs. without instructional explanations). The effects of the different program versions on objective and subjective learning outcomes, on the perceived helpfulness of the program, and on the learning time were analyzed. As learning process data, the written and spoken self-explanations were assessed. The following main results were obtained: Particularly self-explanation prompts had favorable effects on learning outcomes, whereas instructional explanations can reduce the student teacher’s self-explanation activities and thereby the learning outcomes. Whereas “objectively” the most favorable learning outcomes were obtained when only self-explanation prompts were employed, the student teachers perceived their learning outcome best when only instructional explanations were provided. It can be concluded that the status of self-explanation prompts and of instructional explanations is comparable irrespective of the example type presented for learning.     

The design of instructional multimedia in e-Learning: A Media Richness Theory-based approach

The rapid development of computer and Internet technologies has made e-Learning become an important learning method. There has been a considerable increase in the needs for multimedia instructional material in e-Learning recently as such content has been shown to attract a learner’s attention and interests. The multimedia content alone, however, does not necessarily result in significant positive learning performance and satisfaction. Moreover, it is expensive to design and develop multimedia instructional material. There is a lack of extant research to address the critical issue of how to develop effective multimedia instructional content that leads to desirable learning performance and satisfaction. The objective of our paper is to propose and empirically test a model that examines the impact of the fitness of instructional content and media on a learner’s performance and satisfaction.     

Implement web learning environment based on data mining

The need for providing learners with web-based learning content that match their accessibility needs and preferences, as well as providing ways to match learning content to user’s devices has been identified as an important issue in accessible educational environment. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. In order to achieve optimal efficiency in a learning process, individual learner’s cognitive learning style should be taken into account. Due to different types of learners using these systems, it is necessary to provide them with an individualized learning support system. However, the design and development of web-based learning environments for people with special abilities has been addressed so far by the development of hypermedia and multimedia based on educational content. In this paper a framework of individual web-based learning system is presented by focusing on learner’s cognitive learning process, learning pattern and activities, as well as the technology support needed. Based on the learner-centered mode and cognitive learning theory, we demonstrate an online course design and development that supports the students with the learning flexibility and the adaptability. The proposed framework utilizes data mining algorithm for representing and extracting a dynamic learning process and learning pattern to support students’ deep learning, efficient tutoring and collaboration in web-based learning environment. And experiments do prove that it is feasible to use the method to develop an individual web-based learning system, which is valuable for further study in more depth.     

Prediction of student’s mood during an online test using formula-based and neural network-based method

Building computerized mechanisms that will accurately, immediately and continually recognize a learner’s affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence could be combined so as to optimize inferences about affective states during an online self-assessment test. A formula-based method has been developed for the prediction of students’ mood, and it was tested using data emanated from experiments made with 153 high school students from three different regions of a European country. The same set of data is analyzed developing a neural network method. Furthermore, the formula-based method is used as an input parameter selection module for the neural network method. The results vindicate to a great degree the formula-based method’s assumptions about student’s mood and indicate that neural networks and conventional algorithmic methods should not be in competition but complement each other for the development of affect recognition systems. Moreover, it becomes apparent that neural networks can provide an alternative for and improvements over tutoring systems’ affect recognition methods.     

Computer-supported negotiation of course content

Students learn more effectively with personally meaningful tasks. Thus, students learn more if they have a say in deciding what specific topics and examples are being discussed in class. Naturally, the instructor knows what topics are important to cover in a course and which ones might be optional. Finding the right balance between students’ preferences and the instructor’s requirements is not so easy and thus may prevent this kind of shared control of the classroom from being realized. This article describes how the instructor’s and students’ interests can be used to generate a list of course topics that satisfies both parties. However, instead of adding technology to the classroom, technology is used to improve the classroom experiences. Specifically, it is shown how course topics can be assigned to specific students maximizing what is meaningful to the students and satisfies the course parameters as defined by the instructor. This problem can be formulated as a variation of the linear assignment problem and solved with a binary linear program. Results from actual and simulated courses are discussed and generalizations of the topic assignment problem presented.     

Proposing an interactive speaking improvement system for EFL learners

This paper proposes a web-based interactive speaking improvement system for EFL learners. Using fuzzy matching, the system automatically compares the learner’s and the system’s pronunciation and immediately provides corrective interaction whereby the student can improve speaking via continuous imitation and practice at the computer. The system, having been tested on Taiwanese students who needed to achieve a speaking proficiency equivalent to CEF Level A2, is found to be highly effective for the intended purpose.     

Assessing creative problem-solving with automated text grading

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational–statistical machine learning methods to grade students’ natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students’ constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students’ natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen’s Kappa = .65–.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson’s r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.     

A case study of online instructional collaborative discussion activities for problem-solving using situated scenarios: An examination of content and behavior cluster analysis

In some higher education courses that focus on case studies, teachers can provide situated scenarios (such as business bottlenecks and medical cases) and problem-solving discussion tasks for students to promote their cognitive skills. There is limited research on the content, performance, and behavioral patterns of teaching using online discussions and integrated situated scenarios. This case study empirically explored the learning process of adopting collaborative online instructional discussion activities for the purpose of problem-solving using situated scenarios in a higher education course. Thirty-two university students carried out problem-solving activities on case scenarios and problem-solving tasks assigned by the teacher on the discussion forum. Two forms of instructional activities were considered: 1) problem-solving in a given scenario and 2) problem-solving when learners play roles in a given scenario. The activities mentioned above were each implemented for a week.     

Can experts' explanations help students develop program design skills?

This paper reports and experimental investigation of the effectiveness of case studies for teaching programming. A case study provides an “expert commentary” on the complex problem-solving skills used in constructing a solution to a computer programming problem as well as one or more worked-out solutions to the problem. To conduct the investigation, we created case studies of programming problems and evaluated what high school students in ten Pascal programming courses learned from them. We found that the expert's commentary on the decisions and activities required to solve a programming problem helped students gain integrated understanding of programming. Furthermore, the expert's commentary imparted more integrated understanding of programming than did the worked-out solution to the problem without the expert's explanation. These results support the contention that explicit explanations can help students learn complex problem-solving skills.We developed case studies for teaching students to solve programming problems for the same reasons that they have been developed in other disciplines. The case method for teaching complex problem solving was first used at Harvard College in 1870 and has permeated curricula for business, law and medicine across the country. These disciplines turned to the case method to communicate the complexity of real problems, to illustrate the process of dealing with this complexity and to teach analysis and decision making skills appropriate for these problems.     

The formation of learners’ semiosphere by authentic inquiry with an integrated learning object “Young Scientist”

An integrated learning object, a web-based inquiry environment “Young Scientist” for basic school level is introduced by applying the semiosphere conception for explaining learning processes. The study focused on the development of students’ (n = 30) awareness of the affordances of learning objects (LO) during the 3 inquiry tasks, and their ability of dynamically reconstructing meanings in the inquiry subtasks through exploiting these LO affordances in “Young Scientist”. The problem-solving data recorded by the inquiry system and the awareness questionnaire served as the data-collection methods.It was demonstrated that learners obtain complete awareness of the LO affordances in an integrated learning environment only after several problem-solving tasks. It was assumed that the perceived task-related properties and functions of LOs depend on students’ interrelations with LOs in specific learning contexts. Learners’ overall awareness of certain LO affordances, available in the inquiry system “Young Scientist”, developed with three kinds of patterns, describing the hierarchical development of the semiosphere model for learners. The better understanding of the LO affordances, characteristic to the formation of the functioning semiosphere, was significantly related to the advanced knowledge construction during these inquiry subtasks that presumed translation of information from one semiotic system to another. The implications of the research are discussed in the frames of the development of new contextual gateways for learning with virtual objects. It is assumed that effective LO-based learning has to be organized through pedagogically constrained gateways by manifesting certain LO affordances in the context in order to build up the dynamic semiosphere model for learners.     

Cognitive outcomes from the Game-Design and Learning (GDL) after-school program

The Game-Design and Learning (GDL) initiative engages middle school students in the process of game-design in a variety of in-school, after-school, and summer camp settings. The goal of the GDL initiative is to leverage students' interests in games and design to foster their problem-solving and critical reasoning skills. The present study examines the effectiveness of an after-school version of the GDL program using a quasi-experimental design. Students enrolled in the GDL program were guided in the process of designing games aimed at solving problems. Compared to students in a control group who did not attend the program (n = 24), the children who attended the GDL program (n = 20) showed a significant increase in their problem-solving skills. The results provide empirical support for the hypothesis that participation in the GDL program leads to measurable cognitive changes in children's problem-solving skills. This study bears important implications for educators and theory.     

Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration

Given the prevalence of computers in education today, it is critical to understand teachers’ perspectives regarding computer integration in their classrooms. The current study surveyed a random sample of a heterogeneous group of 185 elementary and 204 secondary teachers in order to provide a comprehensive summary of teacher characteristics and variables that best discriminate between teachers who integrate computers and those who do not. Discriminant Function Analysis indicated seven variables for elementary teachers and six for secondary teachers (accounting for 74% and 68% of the variance, respectively) that discriminated between high and low integrators. Variables included positive teaching experiences with computers; teacher’s comfort with computers; beliefs supporting the use of computers as an instructional tool; training; motivation; support; and teaching efficacy. Implications for support of computer integration in the classroom are discussed.     

Improving problem solving and creativity through use of complex-dynamic simulations

This article presents an instructional method to improve problem solving and creativity by employing computer-based simulations. The instructional method described is based upon empirical research conducted by the authors. The simulation presents contextually meaningful problem situations that require learners to analyze and prepare solution proposal(s). Following the learner input, the simulation assesses the proposal and offers back to the learners the consequences of their decisions while also iteratively updating the situational conditions. This type of simulation, unlike conventional simulations that are used for acquisition of knowledge, is complex-dynamic, requiring the learner to fully employ their knowledge base by constructing solutions to domain-specific problems. The focus of complex-dynamic simulations is to improve and elaborate learner cognitive abilities employed in the service of problem solving and creativity.     

Instructing students on effective sequences of examples and problems: Does self-regulated learning improve from knowing what works and why?

Nowadays, students often practice problem-solving skills in online learning environments with the help of examples and problems. This requires them to self-regulate their learning. It is questionable how novices self-regulate their learning from examples and problems and whether they need support. The present study investigated the open questions (1) to what extent students' (novices) task selections align with instructional design principles and (2) whether informing them about these principles would improve their task selections, learning outcomes, and motivation. Higher education students (N = 150) learned a problem-solving procedure by fixed sequences of examples and problems (FS-condition), or by self-regulated learning (SRL). The SRL participants selected tasks from a database, varying in format, complexity, and cover story, either with (ISRL-condition) or without (SRL-condition) watching a video detailing the instructional design principles. Students' task-selection patterns in both SRL conditions largely corresponded to the principles, although tasks were built up in complexity more often in the ISRL-condition than in the SRL-condition. Moreover, there was still room for improvement in students' task selections after solving practice problems. The video instruction helped students to better apply certain principles, but did not enhance learning and motivation. Finally, there were no test performance or motivational differences among conditions. Although these findings might suggest it is relatively ‘safe’ to allow students to independently start learning new problems-solving tasks using examples and problems, caution is warranted: It is unclear whether these findings generalize to other student populations, as the students participating in this study have had some experience with similar tasks or learning with examples. Moreover, as there was still room for improvement in students' task selections, follow-up research should investigate how we can further improve self-regulated learning from examples and practice problems.     

Cognitive tasks in the core content areas: Factors that influence students' technology use in high-school classrooms

Prior studies have focused on general technology use and technology use in domain-general applications and quantity of technology use. Recent evidence suggests that investigations should consider how technology is used in more contextually specific ways, including how technology is used for various cognitive tasks in specific classrooms. The purpose of this study was to examine the ways in which classroom content area and student goal orientation have a coordinated influence for how students used technology to support learning. The sample included high school students in a Midwestern state who were surveyed on their motivation and how they used technology to support learning. The study employed hierarchical linear modelling to examine how goal orientation and classroom content area predicted various levels of Bloom's Digital Taxonomy. Students who adopted mastery-oriented goals were more likely to use technology for various cognitive tasks, especially those at higher levels of complexity. Lastly, the association between mastery goal orientation and some aspects of technology use was conditioned on content area, although effect sizes were small. This study showed that, overall, technology is used differentially across four core content areas. Students in mathematics classrooms used technology less, however much of technology use was evident at lower cognitive levels. Second, students' goal orientation, and in particular their mastery goals influence how technology is used across content areas, and this is marginally conditioned on content area. Technology use should match the instructional context to maximize technology use and students' goal orientation.     

Seeking and providing assistance while learning to use information systems

Throughout their lives, people are faced with various learning situations, for example when they learn how to use new software, services or information systems. However, research in the field of Interactive Learning Environments shows that learners needing assistance do not systematically seek or use help, even when it is available. The aim of the present study is to explore the role of some factors from research in Interactive Learning Environments in another situation: using a new technology not as a means of acquiring knowledge but to realize a specific task. Firstly, we present the three factors included in this study (1) the role of the content of assistance, namely operative vs. function-oriented help; (2) the role of the user’s prior knowledge; (3) the role of the trigger of assistance, i.e. help provided after the user’s request vs. help provided by the system. In this latter case, it is necessary to detect the user’s difficulties. On the basis of research on problem-solving, we list behavioral criteria expressing the user’s difficulties. Then, we present two experiments that use “real” technologies developed by a large company and tested by “real” users. The results showed that (1) even when participants had reached an impasse, most of them never sought assistance, (2) operative assistance that was automatically provided by the system was effective for novice users, and (3) function-oriented help that was automatically provided by the system was effective for expert users. Assistance can support deadlock awareness and can also focus on deadlock solving by guiding task. Assistance must be adapted to prior knowledge, progress and goals of learners to improve learning.     

虚拟现实技术在中学地理课件制作中的应用

虚拟现实技术是一门快速崛起的新型技术,在中学地理课件制作中,运用虚拟现实技术,不但能有效地提高教学效果,激发学生的学习兴趣,而且还能提升教学过程中的信息技术含量.