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Designing a Digital Instructional Management System to Optimize Early Education Ton Mooij
The individualization of curriculum features and the matching of those features with learner characteristics (optimization) may help to stimulate learning processes, in particular for children at risk. In a pilot study in two Dutch kindergartens, specific individualization and optimization conditions were codeveloped with teachers and school management. In order to realize the desired changes, a prototype of a digital instructional management system called the Digital Planning Board was developed, implemented and evaluated. Future research aims at the further development of a pedagogical-didactic kernel structure, integrating instructional management software in new instructional practices.
Instructional theories often focus on individual or small-group learning, although in school practice learning usually occurs in the whole group or class (Collier, 1994). Learning expectancies, complexities, and norms at the class level may then interfere with small-group and individual learning possibilities and potentials. In particular, this is true for learners who are at risk in some respect. Those may be learners who are functioning relatively low compared to most other learners (cf., Walker et al., 1998) or, in contrast, learners who are performing exceptionally high as specified by King et al. (1985). To support developmental and learning processes and to reach desired effects, the characteristics of each individual learner should match with the instructional features of the actual curriculum (Nadolski, Kirschner, van Merriënboer, & Hummel, 2001; van den Akker, 1999; van Merriënboer, 1997). A learner who is significantly “deviating” from other learners may profit most from instructional management designs that individualize and optimize teaching and learning for the whole group. However, paying extra attention to learners’ individual learning processes requires exceptional effort from teachers. As the differences in developmental stage and learning capabilities between children can already be rather large at the start of kindergarten, providing individualized and optimized instruction and preventing a low motivation to learn easily becomes too complicated for a teacher (Jones, Gullo, Burton-Maxwell, & Stoiber, 1998; Skinner, Bryant, Coffman, & Campbell, 1998). In this respect, digital instructional management systems (DIMS) can lend a hand (cf., Chang, 2001; Sinko & Lehtinen, 1999). DIMS
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consist of different kinds of electronic hardware and software, which in combination may support the preparation, execution, and evaluation of networked teaching and learning processes and effects (Lally, 2000). Previous research exploring instructional management for individualization and optimization suggests successive phases for the design, development, and implementation of DIMS in school (Mooij & Smeets, 2001). After the purchase of hardware and software in the first phases, DIMS can be designed to support learning processes in more flexible ways than before. The restructuring requires the transformation of teaching and learning processes by changing the instructional features of the curriculum and the didactic management in such a way that DIMS can support both individualized and small-group learning in and outside school.
education to realize its potential advantages—in particular for children at risk.
Individualizing and optimizing education from the very start of kindergarten, and supporting this process with adequate DIMS, seems a promising approach to preventing a low motivation to learn as well as to solving other learning problems. The main research question is, then, how this transformation process toward individualized and optimized instructional management, and its corresponding DIMS, should be designed and implemented in early
In early education, free playing activities are usually based on children’s own initiatives and choices (Pellegrini & Boyd, 1993). Observations of free playing behavior can teach a lot about a learner’s characteristics, stage of development, and possibilities for further stimulation. Next to free play, specific instructional features are expected to improve a learner’s functioning in a more systematic way (Brush & Saye, 2001; Jewett et al., 1998; Skinner et al., 1998). The in-
Table 1
To answer this question, first, the instructional features of a curriculum that supports individualization and optimization to stimulate all learners is outlined. Second, information is given about the realization of necessary educational changes through the codevelopment of new kindergarten practices with teachers and school management. Third, the design of a supportive DIMS called the Digital Planning Board is discussed, followed by information about its implementation and evaluation. Finally, plans for future research are presented.
CONDITIONS SUPPORTING INDIVIDUALIZATION AND OPTIMIZATION
Categories and conditions to individualize and optimize instructional management
Category
Condition in Early Education
Individualization of Curriculum Features
1. 2. 3. 4.
Optimization: Matching Curriculum and Learner Characteristics
1. Create and stimulate a pro-social pedagogical climate within and between learning groups 2. Use entry characteristics to appoint instructional lines to children 3. Use learning processes and outcomes to improve diagnostic information and progress decisions 4. Use collaborative learning to support childrens’ self-responsibility and self-regulation 5. Concentrate teachers’ coaching on those learners who mostly need this guidance
Screen childrens’ entry characteristics in various ways Differentiate between free play and working along instructional lines Integrate diagnostic tools or progress indicators in instructional lines Organize instructional and learning processes by flexible groupings of learners and teachers 5. Use integrated systems for registration and administration within and between learning groups
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dividualization of curriculum aspects, and optimization by carefully matching the curriculum with learner characteristics, may support development and learning processes—in particular for children who are deviating most from other learners in the group. Table 1 provides an overview of conditions that are important for reaching individualization and optimization.
Individualization
The first condition for individualization is the screening of a child’s entry characteristics when he or she is entering kindergarten. Reliable and valid information about these characteristics is basic to support the further development of the child’s competencies within early education. Also, communication about differences between these characteristics as perceived by the parents and by the kindergarten teacher, and taking adequate curricular or other actions if indicated, can help to prevent motivational, social, emotional or cognitive problems of children (Jewett et al., 1998; Walker et al., 1998). Second, and independent of free play, the concept of “instructional lines” can be used to refer to a specific set of learning materials and activities that are ordered according to instructional difficulty levels or social-didactic aspects. For example, sensory-motor development for four-to-six year olds generally starts with global movement of the whole body, followed by movement of the arms and hands. After that comes preparation for writing, through direction in moving, training of regularity in movement with hands and fingers, and motor exercises, which evolve into preliminary writing. The concept thus denotes a hierarchical arrangement of learning materials, representing specific instructional or playing activities. Third, reliable and valid progress indicators need to be integrated within instructional lines in order to diagnose and evaluate learning processes and outcomes for every learner, from the start in kindergarten onwards. Also, standardized diagnostic tools or age-normed achievement tests need to be included. Each learner’s progress can then be evaluated continuously by individual, social or group-based, and age-
normed diagnostic tools and achievement tests in the whole architecture of instructional lines (cf., Byrne, 1998; Kemp, 2000; Wegerif, Mercer, & Dawes, 1998). Fourth, making the organizational grouping of children more flexible according to learners’ characteristics and the required instructional procedures is another condition to promote school careers continuously (Bennathan & Boxall, 1996; Cooper & Ideus, 1998). Flexible grouping in small learning groups can, for example, be designed on the basis of children’s competency levels, learning styles, or specific instructional preferences or requirements in case of certain handicaps. Moreover, making learner grouping more flexible can also stimulate cooperation between professionals in and outside school, to support children better in their early development (Mangione & Speth, 1998; Mooij, 1999a, 1999b). To conclude, systems for registration and administration should smoothly integrate information about the functioning of individual children within and between different kinds of learning groups. Registration and administration should concentrate on the essentials of the learner’s individual and small group progress on main instructional lines, observations based on free playing behaviors, and comparisons between individual achievements and group-based and age-normed achievements.
Optimization
The second category of conditions concerns optimization, that is, matching curriculum with learner characteristics (see the lower part of Table 1). The first condition regards the socialpedagogical group climate. It is necessary to create and stimulate prosocial pedagogical climates within and between learning groups, to enable positive learning processes and outcomes (Skinner et al., 1998; Walker et al., 1998). Social and cognitive optimization features interact closely, at different instructional and school levels (see for example Collier, 1994; Mooij, Terwel, & Huber, 2000). Second, results on entry characteristics should be used to appoint instructional lines to
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learners. From the viewpoint of learning psychology, entry characteristics are fundamental to a child’s first learning behavior in class; they provide instructional clues for the further stimulation on the same or a higher difficulty level (cf., Byrne, 1998; Kemp, 2000). Third, information about learning processes and outcomes can improve diagnostic and instructional decisions in individual, group-based, and age-normed ways. At least these three evaluation criteria seem to be necessary to provide a complete evaluation of a learner’s functioning and progress. The differentiated evaluation should be discussed regularly between teacher and parents, and if necessary also with external professionals. The goal is to make subsequent pedagogical, learning, and instructional steps more positive and more concrete, and also a common undertaking of all involved parties. Moreover, by using integrated systems for registration and administration within and between learning groups (i.e., one of the conditions for individualization), it becomes possible to measure and check the optimization processes and effects of specific pedagogical or instructional changes, or interventions with specific learners or groups of learners. Fourth, collaborative learning can support childrens’ self-responsibility and self-regulation, in particular in small groups of learners helping each other (Jones, Rasmussen, & Moffitt, 1997). Bergqvist and Säljö (1998) reported about grades 1–3 of four elementary schools in Sweden. The schools use an individualized curriculum in an age-integrated classroom with children from seven to nine years old. The researchers concentrate on learner and teacher cooperation in discussing the children’s weekly planning, that is, learning to self-regulate schoolwork. Their observations reveal that collaboration between learners makes it possible to convey many responsibilities from the teacher to the children because social, pedagogical, and learning roles are intricately related to the instructional, school-wide curriculum organization of both teaching and learning. Moreover, learners are functioning better if they are able to choose instructional or playing activities according to or slightly above their current competency levels. Finally, given the realization of the condi-
tions discussed above, optimization can be achieved further by concentrating teacher coaching on learners who most need this guidance. Jewett et al. (1998) focused on the specific pedagogical and curricular aspects that teachers in kindergarten have to understand in order to help children with special needs. If either such a curriculum or the teachers’ specific assistance are not available, motivational and achievement problems occur for some of the learners, in particular for those who are functioning at significantly lower or higher competency levels than their peers.
DEVELOPING EDUCATIONAL PRACTICE AND A SOFTWARE PROTOTYPE
The educational conditions that are theoretically desirable for individualization and optimization often do not exist in practice (Mooij, 1999a, 1999b). In order to improve this situation, a developmental pilot study was planned in two regular Dutch kindergartens for children aged four to six. In one of these kindergartens the instructional management system consisted of a planning board on the wall, with a different colored column for each day of the week. For some of the curricular activities small groups of learners, corresponding with the children who share one table, were indicated by different logos and colors. Activities that had to be performed by a small group were assigned by placing the tags of these children on a logo representing a certain kind of activity, on a certain day of the week, on the planning board. In the years 1997–2000, the researcher closely collaborated with the teachers and school management of the two kindergartens to create an instructional management system according to the specifications in Table 1. Recent methodology supports a strategy in which users, for example teachers and school staff, collaborate with researchers and other specialists to secure the quality and validity of innovation processes (Blumenfeld, Fishman, Krajcik, Marx, & Soloway, 2000; Clark & Estes, 1999; Kensing, Simonsen, & Bødker, 1998; Remillard, 2000). Wilson (1999) expected that socalled use-oriented strategies “increase the likelihood of successful implementation because
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they take the end use into account at the beginning design stages” (p. 13). In the first stage of the pilot study, attention was focused on rearranging and registering the regular learning and playing materials to clarify the instructional aspects of the curriculum. Also, new features were introduced in combination with these rearrangements and their uses. Parallel to these changes, and in the same collaborative way (cf., Crook, 1998; Ely, 1999), a first prototype of a DIMS was designed and implemented. This Digital Planning Board is running on a stand-alone computer and can collect and represent different aspects of instructional information. The main features of the new practice and the corresponding software prototype will be described in more detail in the next section.
NEW EDUCATIONAL PRACTICE AND THE DIGITAL PLANNING BOARD Individualization Conditions
First, the screening of children’s entry characteristics is carried out in conformance with a psychometrically controlled procedure based on quantitative longitudinal research with a group of 966 four-year-old children (Mooij, 2000). In this procedure, a questionnaire containing seven behavioral rating scales is used by the parents at intake and by the class teacher after the child’s first month in school. The seven scales refer to the (a) social communicative level, (b) general cognitive level, (c) language proficiency level, (d) prearithmetic level, (e) emotional-expressive level, (f) sensory-motor level, and (g) expected educational behavior, respectively. Overviews of the entry characteristics of a child, and comparisons between item- and scale-specific information from the parents and the teacher, can be produced automatically by the Digital Planning Board. Second, the pilot study focused on the development of instructional lines and not on free play. In class, a real instructional line is characterized by a specific logo (e.g., simple geometrical shapes for the prearithmetic line), a specific color, and a corresponding name or text. Learning materials and activities within each
15 line are ordered by difficulty level, with diagnostic tools, activities, and tests included if possible, and located at a specific location in or outside the classroom. Children themselves can go and get the materials, or return them after they are finished. In the Digital Planning Board, an instructional line is represented by the same symbols (logo, color, name) and by pictures or photographs of the learning materials and activities. The teacher can create, change or delete an instructional line. Because the same logo, color, and pictures or photographs are used as with the real instructional line, a child who is interacting with the Digital Planning Board does not need to read to understand the meaning of the digital representation. Digital instructional lines developed in the pilot refer to, for example, language proficiency, (preliminary) arithmetic, motor behavior, general cognitive development, and social-communicative development. The screenshot in Figure 1 shows five quadrants representing these five instructional lines. Each quadrant contains a unique logo (picture), a related color (for example, language proficiency has a red field, preliminary arithmetic a blue field), and the name of the line at the bottom of the quadrant. By clicking on the colored field in a quadrant (see Figure 1), the underlying overview of pictures or photographs of the learning materials and activities included in the instructional line appears. Within the teacher interface, the teacher can add or change learning materials and activities. In addition, variants of instructional lines referring to different developmental levels can be constructed: for example, for learners who are developing in a more or less regular way, for learners who are requiring special or remedial activities, and for high-ability learners who are progressing fast along a particular instructional line. Within the learner interface, a child is shown the materials he or she had been working with last time. It has to be noted that the children play or work with the real three-dimensional materials that are available in or around the classroom, thus with the real instructional line, and not with its digital representation on the computer. This feature seems to suit children of this age group best, and it also overcomes constraints with computer ac-
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16 Figure 1
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A screenshot of the Digital Planning Board showing quadrants with logo, color and name, representing five different instructional lines.
cess. Of course, computer work on another computer than the Digital Planning Board can be included as one of the instructional lines. With regard to the third condition for individualization, diagnostic tools and progress indicators are integrated within instructional lines. An activity can be tagged with an indicator meaning that the child has to go to the teacher in order to continue. For example, an indicator may state that a learner’s level of language competency has to be screened, as a basis for further support and placement decisions. Fourth, the Digital Planning Board makes instructional processes more flexible by allowing smaller groupings of learners. The formation of various kinds of small groups of children can be realized by assigning different groups to different instructional lines, or to different difficulty levels of the same instructional line. Fifth, registration and administration by the Digital Planning Board is carried out with respect to different aspects. It is possible to get
an overview of the content of an instructional line, or variants of an instructional line, at a specified level of difficulty. Also, administrative information about a child or teacher can be included by adding a new child or teacher, removing a child or teacher, or changing existing information about a child. For example, the teacher can register learners by including their photographs in the database. The photographs of all children in one class can be shown at one screen. Moreover, the Digital Planning Board allows automatic logging of activities and time on task for each of the learners working with it.
Optimization Conditions
A first feature with regard to optimization concerns the creation and stimulation of a prosocial pedagogical climate within and between learning groups. Teachers try to realize this by formulating positive rules of conduct for children
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assisting other children in working with the Digital Planning Board. They also emphasize positive mutual control of those rules of conduct by the children getting and giving this computer support, which is important to prevent antisocial behavior (Mooij et al., 2000; Walker et al., 1998). Second, the findings on entry characteristics of children are used to appoint particular instructional lines to specific learners. More generally, a teacher can place any child at any place within the set of available digital instructional lines. A teacher can also insert and order pictures of specific learning materials and activities, and assign different activities to different learners. For example, a teacher can successively select a child, select a particular digital instructional line for this child, select a difficulty level, or select a particular variant of this line (remedial, regular, fast, or some specific material), and finish by saving the changes made. The next time this child accesses the Digital Planning Board, the choices to play or work are determined by the teacher’s instructional management decisions. In this way, the learner’s choices and consequent activities are regulated by the Digital Planning Board. Third, learning processes and outcomes for an individual child or small group of learners can be used to improve diagnostic information and progress decisions. Given the earlier learning processes and outcomes of the learner, it is the teacher who decides which learners are working with particular instructional lines, including particular diagnostic tools. If desired, the teacher can involve children, other teachers, the parents, or a professional evaluator from outside school to diagnose learning processes, interventions, or outcomes. Fourth, collaboration between children is possible, and often stimulated, to support learning, self-responsibility, and self-regulation. To consult the Digital Planning Board, a child can click his or her own photograph on a screen that contains photographs of all children from the same class. Then, in a next screen, the photograph and name of this child are shown as a check, together with the learning materials that the child had been working on earlier. and three icons. Each icon illustrates one possibility: (a) the
child is ready and wants to stop; (b) the child wants to be advised on a new activity, or (c) the child made a wrong choice and wants to go back to a previous decision. The child has to know which digital assignment corresponds with which playing activity or instructional activity in the classroom. In practice, a child can always ask for help from a peer or the teacher, if necessary. Also, a particular activity for one child can be made valid for another child or for a (small) group of children, because the teacher has scheduled it as group work. This is signaled by tagging an indicator to the specific activity for one of these children, who has to include the teacher to do this activity. The teacher can then instruct group work, make specific observations, and so forth. Finally, the instructional support that is provided by the Digital Planning Board allows the teacher to concentrate more on children who need this guidance, if necessary in a small group. Moreover, a teacher can change or extend instructional activities or playing activities within an instructional line, to improve curriculum or learning processes or to check their desired effects on one or more learners.
IMPLEMENTATION AND EVALUATION
The first individualization condition, screening children’s entry characteristics and discussing these characteristics with the parents, could be handled well by the teachers. At first, however, some guidance of the teachers was necessary— in particular with respect to approaching and involving parents. This new feature of instructional management certainly seems to be worthwhile. Problems or potential risk characteristics of a child get more preventive and more supportive pedagogical attention, if necessary by the early inclusion of specialists from outside the school. This means that, by incorporating this feature into early education, the prevention of learning problems through cooperation between parents and teachers may improve considerably in comparison with current practice. In the first years of this pilot study, differentiating between scheduled work on instructional lines and free play required much guidance of the teachers. Gradually, teachers came to see the
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18 importance of this distinction, in particular because of the didactic clarifications that were introduced by the use of instructional lines. Highly relevant in the teachers’ learning process were the observed changes in the children’s development and learning processes and the effects they perceived for the children deviating most from the other learners in the class. Systematic discussions and longitudinal collaboration with respect to specific children revealed many new insights, which could be used subsequently in rearranging practices in more pedagogical and preventive ways. More than before, learners can now get systematic and immediate curricular support that is adjusted to their own level of competency. The integration of diagnostic tools and progress indicators within instructional lines resulted in teachers learning to differentiate in a pedagogical sense between curriculum features on the one hand, and learning activities and effects on children on the other hand. According to the teachers, this enabled them to use the learning materials better than before to promote the optimal functioning of children. What was lacking, however, was a coherent reference pattern to evaluate the individual learner’s level of functioning, learning progress and learning outcomes independent of age norms. It seems that a generally valid kernel structure of pedagogicaldidactic features of the curriculum is necessary to support age-independent individualized evaluations, in addition to the usual age-normed evaluations. The use of kernel structures will be further discussed in the section on Next Steps. The organization of instructional and learning processes by flexible groupings of learners was functioning smoothly. The expected positive outcomes, however, could only be realized to a limited degree. The main problem was that teachers needed much more time than before to remediate and instruct individual children, in particular those children differing most from the other learners. Depending on the characteristics of the learners within a class, regular class size (25–30 children) can impose severe time restrictions in this respect. It was tried to resolve the time problem partly by calling in parents to instruct specific children. This was not really satisfying, however. A higher teacher-child ratio or
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professional assistance is necessary in order to adequately deal with differences between children—especially because those differences are already present at the start of kindergarten. To conclude our discussion of conditions for individualization, it should be noted that the integrated registration and administration facilities were developed only to a preliminary degree in the prototype of the Digital Planning Board. A first step has been set toward automatic logging and monitoring of learner, class, and school results. It has been shown that DIMS in kindergarten can help to diagnose and assess each child’s progress in both individual and group-normed ways, and to construct specific, individualized instructional lines. A next step could include DIMS to support networked instructional information for different kinds of users, such as teachers, parents, school management, and learners (see further below; see also Tymms, Merrell, & Henderson, 2000). With respect to optimization, attention has been paid to the creation and stimulation of a prosocial pedagogical climate within and between learning groups. This topic was addressed in the specifications of concrete collaborations between learners, and in the positive rules of conduct to be used between learners or between learners and other persons. In a class, this issue can get attention with respect to any didactic specification. In the future, techniques and instruments from instructional or school intervention projects could be integrated, also with respect to the measurement of relevant indicators at different educational levels (cf., Mooij et al., 2000). Results on learners’ entry characteristics were used by the Digital Planning Board to appoint specific instructional lines to children. By using this feature of the prototype the teachers discovered that the available amount of learning and playing materials acutely needed to be extended. This insight was based on the need to take care of the different initial levels of competence in adequately differentiating instructional lines. The differences between learners were much larger than was accounted for in the available learning materials, activities and diagnostic tools. A measurable consequence was that a great deal of the next year’s school budget was
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spent on buying new learning materials and toys for playing.
to the situation without the Digital Planning Board.
Gathered information about developmental and learning processes and learning outcomes could be used to improve diagnostic tools, behavioral indicators, and progress decisions about learners. In this respect the prototype of the Digital Planning Board suggests a specification of three kinds of activities within an instructional line: (a) regular learning activities, (b) evaluative activities as defined by the teacher, and (c) age-normed diagnostic activities. This issue will be further elaborated in the section on Next Steps.
Generally, the implementation of the prototype of the Digital Planning Board reflects a continuous collaboration between kindergarten teachers, school management, parents, the research team, and software developers. All partners experienced this collaboration as very worthwhile. The codevelopment of new concepts and tools in early educational practice was a valuable learning process for all persons involved.
Collaborative learning was used not only to support childrens’ learning but also to support their self-responsibility and self-regulation. It was found that moderate-to-high ability children of four, five or six years of age quickly learned to operate the Digital Planning Board. If desired, they could also assist other children in learning to use the program. Slower developing children usually had more difficulty in learning to use the program but were assisted by their peers and parents, if possible. In this respect much depends on the teacher’s pedagogical and didactic quality to stimulate collaborative activities between young learners, which is also basic to effective collaborative learning later on in elementary education.
NEXT STEPS
To conclude our discussion of optimization issues, it should be noted that concentrating teachers’ coaching on those learners who most need this guidance only proved to be possible to a limited degree (see also the discussion above). A higher teacher-student ratio, or more professional support, or both are necessary, given the characteristics and needs of the learners. It is important to base the available amount of pedagogical and didactic support on the actual characteristics and potentials of the children within a class. And the support should be adjusted to the needs of individual learners. The pilot study reveals that this usually does not occur for the children deviating most from the other learners. However, because children’s selfmanagement was stimulated by the Digital Planning Board, teacher opportunities to devote more time to the learners who needed this assistance most were enlarged somewhat, compared
Given the outcomes of the pilot study, next development steps will concentrate on the further elaboration and realization of the instructional conditions that have been specified in Table 1. Within the context of all 10 conditions, in the future 2 conditions need to be in the focus of integrated practice and software development. The first condition pertains to the integration of diagnostic tools and progress indicators within instructional lines (i.e., Condition 3 for individualization). One of the outcomes of the pilot study indicates that norm-referenced diagnostic tools and achievement indicators in instructional lines can be conceptualized as representing a kernel structure of pedagogicaldidactic features of the curriculum. This kernel structure can be based on the architecture of agenormed means of screening instruments and tests for children in early education. Use of an individual learner’s scores, and the comparison of these scores with the means of the class and with age-normed means, allows for a coherent diagnostic evaluation of the learner’s characteristics in individual, social, or group, as well as age-normed respects. A first model of a pedagogical-didactic kernel structure for kindergarten may be sketched as follows. Entry characteristics (see above) may be measured on the 7 screening scales completed by the parents and, after one month, by the teacher, yielding 14 scales. Age-normed means and heterogeneities for the 14 scales can be taken from Mooij (2000). Furthermore, agenormed observation or test results on learning
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20 or playing behaviors can be based on information from the parents, the teacher, another professional, and specific instructional lines characterizing the kindergarten curriculum. Longitudinal data collection with each child can be used to construct indicators about: (a) the individual learner’s scale or test means and heterogeneities, (b) discrepancies with the class means and heterogeneities, and (c) discrepancies with the age-normed means and heterogeneities. In this way teachers, parents, and school management obtain multidimensional, multilevel perspectives on learner characteristics and progress. This will make it easier to handle flexible groupings of learners, if desired, independent of their age. Moreover, the kernel structure can be used locally to create instructional lines fitting into actual teaching or learning situations. The kernel indicators also form a common frame of reference for the preventive engagement of an external specialist, such as a school psychologist (cf., Griffin & Beagles, 2000). The main advantage of an educational system based on such a kernel structure is the preventive, curricular support for individual learners in positive group contexts. Essential is the continuous, multifold evaluation scheme (individual progress, progress relative to the small group or class, and progress relative to the agegroup). In contrast, the regular Dutch student monitoring system now only emphasizes a comparison with the age-group, which in the long run usually has negative consequences for the motivation and achievement of children at risk (cf., Collier, 1994; Kemp, 2000). It is therefore worthwhile to elaborate the model of a kernel structure of pedagogical-didactic features into an upward direction, that is, into elementary education and above. The second main point for future research pertains to the use of integrated registration and administration systems within and between learning groups (i.e., Condition 5 for individualization). The next development phase will concentrate on individual and small group digital registration of initial learning characteristics, the automatic selection of instructional features and processes, and diagnostic tools for learning process and achievement evaluation
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within the architecture of instructional lines. The existence of a pedagogical-didactic kernel structure is conditional to this second step but, from the other side, the availability of corresponding software will certainly stimulate the development of a kernel structure as well as its assumed optimizing effects for children at risk in particular. Given the outcomes of the pilot study, the first design features of the next version of the Digital Planning Board can be schematized as in Table 2. In the first column of Table 2, seven different levels of the educational system are recognized. At each level different but related instructional features are functioning, as specified in the second column of Table 2 (see also Mooij et al., 2000). The third column lists different kinds of users who choose, use, develop, or evaluate different instructional features, processes, or outcomes. Basing the new version of the software on Internet technology allows for integrated registration, planning, execution, evaluation, and administration, that is, networked learning in different environments. In a follow-up project in early, primary, and secondary education this software will be entitled “Learning In Networked Environments” (LINE). The software will also be used to build up a pedagogicaldidactic kernel structure for different educational sectors (see http://www.scholen.net/line).
DISCUSSION
In a four-year project, attention was paid to the improvement of early educational practice and to the design of a supportive DIMS. While leaving situations for free play intact, the goal of this pilot study was to stimulate individualization and optimization in early education, in particular for children who are potentially at risk. To check the theoretical assumptions about the instructional relevance of specific individualization and optimization conditions, instructional lines with respect to ordinary learning and playing materials and activities were codeveloped with teachers and school management in two kindergartens. The ordering of learning materials and diagnostic tools occurred with specific symbols (logo, color, name) in order to
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Table 2
First Design Features of Software Supporting “Learning In Networked Environments” (LINE)
Educational level
Possible instructional features
Potential users
National
Pedagogical-didactic kernel structure: age-normed test means, heterogeneities
Schools, external professionals, parents, policy instances, research
Regional or group(s) of schools
Selection out of kernel structure
Schools, parents, policy instances, external professionals, research & development, (specialist) teachers
School or kindergarten
1. Conceptualizing instructional lines 2. Assigning materials and activities 3. Assigning diagnostic tools and evaluation 4. Groupings of learners or teachers 5. Assigning learners to lines
Schools, (specialist) teachers, parents, external professionals, policy instances, research & development
Unit within the school
4. Smaller groupings of learners or teachers 5. Assigning learners to lines
Teachers, specialist teachers, learners, parents, external professionals, research & development
Teacher or class
4. Smaller groupings of learners or teachers 5. Assigning learners to lines 6. Matching materials to individual learners or (small) groups of learners 7. Planning instructional lines for individual learners or (small) groups 8. Checking, coaching and evaluation
Teachers, learners, parents, external professionals, research & development
Small group of learners
9. Networked learning and evaluation 10. Start again with learning, according to (6)
Learners, teachers, parents, external professionals, research & development
Individual learner
9. Networked learning and evaluation 10. Start again with learning, according to (6)
Learners, teachers, parents, external professionals, research & development
make them recognizable and understandable for young children. Also, a prototype of a Digital Planning Board was developed, used, and evaluated in two classrooms to check the functioning of the new educational practice. Implementation and evaluation of these integrated instructional changes lead to the conclusion that learning processes in early
education can be improved. Compared with traditional early education, a better match between learners and curriculum and more constructive learning processes can be realized in collaboration between teachers and management, children, parents, and specialists from outside school. However, given the developmental focus of the pilot study and the im-
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plementation in only two kindergartens, it is not yet possible to present systematic quantitative information on improvement of learning processes or outcomes with learners. At first higher research efforts and more systematic innovation support will be needed, in particular to develop (a) a pedagogical-didactic kernel structure, and (b) integrated systems for digital registration and administration, based on different kinds of progress indicators. These two features constitute the main goals of a follow-up project which will be carried out in kindergarten, elementary education, and secondary education.
Ton Mooij [
[email protected]] is with the University of Nijmegen, The Netherlands. Correspondence concerning this article should be addressed to Ton Mooij, University of Nijmegen, Institute for Applied Social Sciences, Toernooiveld 5, 6525 ED Nijmegen, The Netherlands.
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