Dynamic and emergent information systems strategy formulation and implementation

Early attempts to formulate information systems (IS) strategies concentrated on the analytical task of deriving IS strategies from business plans. The limitations of the static plans that often resulted from these formal studies were, however, soon discovered. The critics suggested informal and incremental planning to ensure flexibility, creativity and strategic thinking to comprise emergent strategies as well as planned strategies.In previous IS planning research, there appears to be a contradiction between the published planning methods and the generally held views about effective implementation of IS planning process. The explicit methods described in IS literature predominantly assume a comprehensive IS planning process. Despite the fact that many researchers consider incremental approaches to be more effective, methods that can be used to facilitate incremental IS planning are few, not detailed enough and not comprehensive.The four cycles method introduced in this paper attempts to combine the strengths of both the comprehensive and incremental planning to be able to recognise emerging trends and to make an e-business strategy. The method provides a basic schedule for organising planning activities. IS planning is seen as a continuous process that is periodically adjusted to the expectations of the participating managers. Practising managers can use the method to facilitate implementation of an incremental and continuous IS planning process. For e-business strategy research, the paper provides a theoretically based method that can be tested in future action research projects.The first results of conducted action research show that the method should not be used as a checklist but as a choice list. Each period had a constant focus on external developments and the fit with internal possibilities. The method provided a flexible and dynamic basis for actions. The emergent nature of the changes and the difficulty of formalising creativity and innovation placed restrictions on the planning process. We learned that a thematic approach where each cycle is given a creative subject helped to “open up” the users in the organisation. Future research should focus on the inter-organisational nature of e-business strategy. If it is difficult to get top management participation, it will be even more difficult with more organisations involved.     

Effects of within-class ability grouping on social interaction,achievement, and motivation

This study examined how grouping arrangements affect students achievement, social interaction, and motivation. Students of high, average and low ability were randomly assigned to homogeneous or heterogeneous ability groups. All groups attended the same plant biology course. The main results indicate that low-ability students achieve more and are more motivated to learn in heterogeneous groups. Average-ability students perform better in homogeneous groups whereas high-ability students show equally strong learning outcomes in homogeneous and heterogeneous groups. Results on social interaction indicate that heterogeneous groups produce higher proportions of individual elaborations, whereas homogeneous groups use relatively more collaborative elaborations. In the discussion, these differences in social interaction are used to explain the differential effects of grouping arrangements on achievement scores. Practical implications are discussed and topics for further research are advanced.     

Giving feedback on peers’ concept maps as a learning experience: does quality of reviewed concept maps matter?

Learning Environments Research - Giving feedback to peers can be a powerful learning tool because of the feedback provider’s active cognitive involvement with the products to be reviewed. The...     

The influence of prior knowledge on experiment design guidance in a science inquiry context

Designing and conducting sound and informative experiments is an important aspect of inquiry learning. Students, however, often design experiments that do not allow them to reach conclusions. Considering the difficulties students experience with the process of designing experiments, additional guidance in the form of an Experiment Design Tool (EDT) was developed, together with reflection questions. In this study, 147 pre-university students worked in an online inquiry learning environment on buoyancy and Archimedes’ principle. Students were randomly assigned to one of three conditions, each of which contained a different version of the EDT. Since students’ prior knowledge has been found to influence the amount and type of guidance they need, the versions of the tool differed with respect to the level of guidance provided. A pre- and post-test were administered to assess students’ conceptual knowledge. No overall differences between conditions were found. In a subsequent analysis, students were classified as either low, low-intermediate-, high-intermediate, or high prior knowledge students. For Archimedes’ principle we found that low-intermediate prior knowledge students gained significantly more conceptual knowledge than low prior knowledge students in the fully guided condition. It is hypothesised that students need at least some prior knowledge in order to fully benefit from the guidance offered.     

Updating Latent Class Imputations with External Auxiliary Variables

Latent class models are often used to assign values to categorical variables that cannot be measured directly. This “imputed” latent variable is then used in further analyses with auxiliary variables. The relationship between the imputed latent variable and auxiliary variables can only be correctly estimated if these auxiliary variables are included in the latent class model. Otherwise, point estimates will be biased. We develop a method that correctly estimates the relationship between an imputed latent variable and external auxiliary variables, by updating the latent variable imputations to be conditional on the external auxiliary variables using a combination of multiple imputation of latent classes and the so-called three-step approach. In contrast with existing “one-step” and “three-step” approaches, our method allows the resulting imputations to be analyzed using the familiar methods favored by substantive researchers.     

Learner vs. System Control in Using Online Support for Simulation-based Discovery Learning

Learning in discovery environments, such as simulations, requires both planning and complex inference processing from learners. Studies evaluating simulations learning have indicated that, without added instructional support, simulation learning knowledge gains are disappointing and/or unclear. With regard to the problems that learners could encounter, we feel, on the one hand, that learners should be supported in their discovery processes whenever possible. On the other hand, we believe that learners' own responsibility in the learning process should be emphasised. The discovery environment in the present study is called CIRCUIT, in which the behaviour of current and voltage sources in electrical circuits is simulated. CIRCUIT includes three main types of instructional support: model progression, assignments, and explanations. Two experimental conditions were created. Although these conditions were similar with respect to the support, they differed with respect to the amount of freedom given to the subjects. That is, the first group of subjects was free to choose their own sequence exploring the simulation environment. In the second group, the sequence was largely controlled by the environment. In determining the instructional value of the interaction, special attention is paid to acquiring intuitive knowledge as compared to definitional knowledge. The evaluation followed a pretest-posttest design. Results showed no gain in definitional knowledge but a gain in intuitive knowledge. No major differences between the experimental groups were detected in the posttest results or in the interaction processes, a cognitive load measure, and the subjective ratings. This revised version was published online in July 2006 with corrections to the Cover Date.     

Promoting Self-Directed Learning in Simulation-Based Discovery Learning Environments Through Intelligent Support

Providing learners with computer-generated feedback on their learning process in simulation-based discovery environments cannot be based on a detailed model of the learning process due to the “open” character of discovery learning. This paper describes a method for generating adaptive feedback for discovery learning based on an “opportunistic” learning model that takes the current hypothesis of the learner and the experiments performed to test this hypothesis as input. The method was applied in a simulation–based learning environment in the physics domain of collisions. Additionally, the method was compared to an environment in which subjects received predefined feedback on their hypotheses, not taking the experimentation behavior into account. Results showed that overall both groups did not differ on knowledge acquired. A further analysis indicated that, in their learning processes, the learners in the experimental condition built upon their intuitive knowledge base, whereas the learners in the control condition built upon their conceptual knowledge base. In addition, measures of the learning process showed that the subjects in the experimental condition adopted a more inquiry-based learning strategy compared to the subjects in the control condition. We concluded, therefore, that providing learners with adaptive feedback had a different and beneficial effect on the learning process compared to more traditional predefined feedback.     

List of Guest Reviewers—Volume 15

This study compared the affordances of 4 multimedia learning environments for specific learning processes. The environments covered the same domain but used different instructional approaches: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry learning. Although they all promote an active attitude, they differ in the specific learning processes they intend to foster. In earlier research (Eysink et al., 2009 Eysink, T. H. S., de Jong, T., Berthold, K., Kolloffel, B., Opfermann, M. and Wouters, P. 2009. Learner performance in multimedia learning arrangements: An analysis across instructional approaches. American Educational Research Journal, 46: 1107–1149. [Crossref], [Web of Science ®] , [Google Scholar]), we found that learners involved in self-explanation-based or inquiry learning had the highest learning outcomes. In these approaches learners were required to generate (parts of) the subject matter, from which we concluded that they presumably stimulated learners to elaborate. Therefore, in the present study we expected that learners involved in self-explanation-based or inquiry learning would engage in more learning processes connected to elaboration than would learners involved in hypermedia or observational learning. Forty participants worked through the learning environments while thinking aloud; their protocols were coded using a generic learning processes scheme. Results showed that self-explanation-based learning and inquiry learning led to greater engagement in learning processes in general and more elaborative processes in particular. The results suggest that elaboration is indeed the key process explaining differences in learning across different instructional approaches within multimedia learning environments.     

Presenting domain information or self-exploration to foster hypothesis generation in simulation-based inquiry learning

This study investigated the effects of presenting domain information (basic information about the domain) either together with or instead of offering exploratory practice (an exploratory opportunity in a simulation-based representation of the learning domain) prior to inquiry learning for facilitating students' hypothesis generation and subsequent inquiry processes and their knowledge acquisition. Secondary school students (n = 118) completed a simulation-based inquiry task on force and motion. They were randomly assigned to one of four conditions: the D + E condition (n = 29), in which domain information and exploratory practice were available; the D condition (n = 30), in which only domain information was available; the E condition (n = 32), in which only exploratory practice was available; or the C condition (n = 27), with no support at all. Students' knowledge was measured with a pre- and posttest and a test on knowledge of variables. Inquiry processes were inferred from information students entered in a Hypothesis Scratchpad and an Observation tool, and from a final summary that they had to write. Results indicated that providing students with domain information alone helps to foster their knowledge of variables before generating hypotheses and leads to knowledge acquisition. The results also showed that the opportunity to explore before experimenting does not affect students' inquiry behavior or learning performance, even when combined with providing students with domain information.