Research Article

How Outreach Iearning at Authentic Waste Management Environments Interacts with Cognitive Knowledge Dimensions

Bogner Franz X* , Fremerey Christian
University of Bayreuth, Z-MNU (Centre of Mathematics & Science Education), Dep. of Biol. Education, 95447, Bayreuth, Germany
*Corresponding author:

Bogner Franz X, University of Bayreuth, Z-MNU (Centre of Mathematics & Science Education), Dep. of Biol. Education, 95447, Bayreuth, Germany, Email: franz.bogner@uni-bayreuth.de

Keywords:

Inquiry-based learning, Outreach learning site, Informal learning, Cognitive knowledge dimensions, General ecological behavior

Worldwide, approximately 3.5 million tons of waste are produced daily, and just about half of this amount is committed to recycling or reuse. As individuals too often are still unaware about their waste contribution, this remains an important issue in formal and informal education. On an authentic waste incineration site, a student-centered inquiry-based learning program within a guided on-site tour was applied. 471 fifth graders participating in our informal outreach module learned significantly about subsequent incineration processes and their consequences. Our empirical analyses focused on three dimensions of newly acquired knowledge: system-knowledge, action-related knowledge, and effectiveness-knowledge, which have been shown to contribute to individual attitude bases and conservational behavior. As hypothesized, all three types of knowledge were shown affected. Consequently, an appropriate inclusion of educational modules into authentic real life processes was shown to be effective regarding both cognitive knowledge transfer and even in concluding the relationship with general ecological behavior. Implications for outreach, education initiatives are discussed, recommendations are highlighted.

Our current style of living, together with the fast growing world population, generates a major amount of waste, currently approximately 3.5 million tons per day (Hoornweg, Bhada-Tata & Kennedy, 2013). While 55 percent of our incidental waste is recycled or reused, the rest remains in our environment, as the current infamous example of the Great Pacific Garbage Patch drastically points out. Hoornweg, Bhada-Tata, and Kennedy (2013) have described the increase in waste production as the highest of all polluting factors (such as greenhouse gasses), and predict approximately 11 million tons of solid waste per day in the year 2100. As waste avoidance seems to present the most effective response, the European legal framework for regulating the handling of waste takes this into consideration by favoring a waste hierarchy: waste prevention, waste reuse, waste recycling and “harvesting” the remainder for energy production (EU Official Journal, 2008). Volk &Lippelt (2011) regarded waste deposits in landfills as inappropriate ways to deal with the garbage problem by pointing to the better-known options: For instance, waste incineration plants focus on energy recovery. Nevertheless, waste resources and or a waste of resources frequently are issues of dispute (Aadland&Caplan 2006). The same is true for formulating recycling goals and how do convince individuals to recycle (Bagozzi&Dabholkar 1994; Black &Cherrier 2010). In the case of our study site, a well-established regional waste transfer system annually collects about 390,000 tons of waste at just one central plant. The collection system is largely rail-based to avoid road traffic congestion and emissions.

Facts are needed as all of us contribute to local waste problems. However, the mere existence of waste accumulation produced by others contributes to substantial public alertness, especially as one central site accumulates the waste of total regions. Without accompanying education initiatives, public acceptance remains too often low and resistance to ever growing waste amount does not grow. Especially young people are the preferred targets of educational efforts within this context. Although formal propositions within existing syllabi already exist, outreach offers may even better support three incentives: (i) to help schools to teach this issue appropriately, (ii) to provide appropriate knowledge on how the problem is solved locally, and (iii) to increase the acceptance of waste management (e.g. Kaibel, Auwärter&Kravcik, 2006). Fremerey and Bogner (2015), for instance, have shown for guided factory tours a reasonable acquisition of sustainable knowledge, even when, due to school schedules, such interventions are of short duration. Tight school schedules often only allow short-term educational programs, as a maximum of half- or one-day interventions match best with school requirements. Authentic environments outside of classrooms provide substantial additional value (e.g., Scharfenberg&Bogner, 2013a; Bogner, 2002; Bogner & Wiseman, 2006; Sturm & Bogner, 2008). Fancovicova&Prokop (2011), comparing outdoor with classroom learning, reported higher knowledge acquirement levels and long-lasting learning effects for the experimental group. This is in line with, for instance, Kossack and Bogner (2012) or Fremerey and Bogner (2014) who reported for short-term/one-day outreach educational units with authentic experience environments positive cognitive learning effects and a positive effect on attitudes.

Early exposure to meaningful science education (whether formal or informal) are supposed to positively affect student variables such as interest, the motivation of even likelihood to choose science careers later on (Tai et al., 2006; Beggs et al., 2008). Within the available set of interaction choices, inquiry-based hands-on learning activities may best develop an appropriate interest in young people (Schmid&Bogner, 2015; Gibson & Chase, 2002). Additionally, any likelihood of choosing science careers depend on the preferences seems laid and supported in early school (Dabney et al, 2002). The current dilemma of the majority of students not opting for science may largely originate in the failure of classroom contents to connect sufficiently with everyday life (Miller, Blessing & Schwartz, 2006). Consequently, in overcoming this apparent gap by linking an everyday problem with its current solution will substantially contribute to the engagement of students and prepare them for future career choices (Hall et al., 2011). Recent studies such as Sheridan et al. (2014) support this, where a provision of personally meaningful issues was shown to effectively shift individual levels, for instance, of interest, autonomy or competence beliefs. Learning activities in interest-driven approaches may are supposed to support individual interests, especially when embedded in everyday life experiences (Petrich et al. 2013) and in the long-term may intervene positively with students’ attitudes towards science (Gibson & Chase 2002). The lack of such connection may reduce identification with science learning in retaining negative perceptions towards science or fail to provide opportunities to develop personal values while tackling with a scientific problem (Basu& Barton, 2007; Jack & Linn, 2014).

Knowledge architectures frequently are discussed within the pedagogical community. For instance, Frick, Kaiser, and Wilson (2004) proposed a model with three types of knowledge where cognitive knowledge acquisition is generally regarded as an important requirement to lead to desired ecological behaviors. Cognitive understanding of environmental problems is understood as a way to change environmental preferences settings: An appropriate provision of the information is assumed to help an individual to protect the environment (Kaiser & Fuhrer, 2003; Kaiser, Hübner&Bogner 2005). However, before someone decides to act, individual knowledge of available actions is needed. Kaiser et al. (2008) proposed three dimensions of environmental knowledge: (i) System-knowledge (SYS) defined as factual knowledge includes knowledge bases about natural processes or environmental systems. Additionally, this type of knowledge serves as a basis for both the other types of knowledge. Frick et al. (2004) described system-knowledge as "knowing what". (ii) Action-related knowledge (ACT) contains knowledge about possible courses of action to a certain topic. These options may be important for individuals or for society. Frick and colleagues (2004) defined, action-related knowledge as "knowing how". (iii) The third dimension defined as effectiveness-knowledge (EFF) is used to assess the effectiveness of various options for action, to compare them and to select the best option in each case. Effectiveness-knowledge is the most complex type of knowledge and requires the other two types of knowledge as a foundation. The model of Kaiser and colleagues (2008) integrates the relations of the three environmental knowledge types with each other and shows their relationship to general ecological behavior (GEB; Kaiser, Oerke&Bogner, 2007): Basic information about a topic contributes to effectiveness judgements (Roczen et al., 2014). However, system-knowledge alone may show no direct effect on ecological behavior (Frick et al., 2004), in contrast to action-related and effectiveness-knowledge. System-knowledge may only indirectly influence ecological behavior, because of its direct relationship to the other two dimensions of knowledge (Frick et al., 2004). Action-related knowledge may directly impact, effectiveness-knowledge: several actions must be learned before comparing and evaluating effectiveness becomes possible. Through the relationships between the types of knowledge and ecological awareness (Kaiser et al., 2008), it is advantageous when teaching modules include all three types of knowledge. A knowledge, increase in all dimensions of knowledge may provide a suitable basis for ecological behavior. The mediation of environmental knowledge in learning programs is, therefore, an important and meaningful goal.

The objectives of our present study were two-fold: First, to analyze all three knowledge dimensions within the described outreach 4-R waste module, to approve the questionnaire’s reliability and validity and to monitor short- and long-term achievement levels within all knowledge levels. Second, to analyze potential relationships: (a) of all knowledge scores of the three testing cycles, (b) of the knowledge increase with general ecological behavior (GEB).

A sample of 471 fourth graders (average age of 10.3 years; SD = 0.5) participated in our study following a pre-/post-test design. 49.5% of our sample were females. The gender ratio was balanced and none of the pre-scores produced gender-dependent significant differences. A test-design with pre-, post- and retention test was applied: The pre-test, two weeks before the intervention, the post-test, directly after, and the retention test about six weeks later. A paper-and-pencil-test with 23 knowledge-items were applied, randomly mixed at the three testing schedules in order to avoid potential memory effects. The items covered all three environmental knowledge dimensions, system knowledge (SYS), action-related knowledge (ACT) and effectiveness knowledge (EFF). An expert team of educators independently classified the questions into the three knowledge dimensions. Each multiple-choice item consisted of four potential answers, of which only one was correct. The knowledge-questionnaire was completed three times, one two weeks before the intervention, a second directly after the intervention and a third six weeks later. The participants were never aware of the repeated testing schedules. Before the intervention, participants completed the GEB-scale.

Responses to the knowledge questions were sum-scored, a correct answer scored with 1, an incorrect with 0. The GEB scale employs a response scale of 5 to 1 (‘completely true’ (5); ‘undecided’ (3); ‘completely false’ (1) (Kaiser, Oerke&Bogner, 2007). The semantic differential followed a 5-digit response pattern ranging from ‘easy to understand’ to ‘difficult to understand’. For SPSS analyses, the version IBM SPSS Statistic 22 was used and for Rasch-analyses the program ConQuest 3.A test-re-test group of 70 pupils (of similar age group and educational level) completed the same multiple-choice tests as the treatment group, but without participation in our intervention. The waste incineration site of our present study collects garbage from approximately 1,856,000 inhabitants summing annually to about 390,000 tons. Delivery to the incineration site is mainly rail-based. The first educational stop is at the unloading station where large cranes, transfer the waste into a huge silo with access to four oven lines. Incineration achieves temperatures of 850-1000°C, no other additives are necessary for the combustion. The remaining waste is collected in a slag bunker where iron parts are separated by magnets. The incineration process feeds a steam generator for supplying neighboring industry and houses as well as for producing electricity. The educational intervention lasted about 150 minutes at the following modules: First, after an introduction, small groups of pupils separated a garbage heap by coming to individual decisions about separation guidelines. After a short safety instruction, 65 minutes guided unit through the plant site focused on the major steps of the incineration process. A final encounter wrapped up and closed the outreach activity.

By using the dichotomous Rasch-model the items’ reliabilities and its difficulty distribution were analysed (Bond & Fox, 2010). In the Wright-map (see Figure 2), the ability of participants is displayed on the left side and on the right side the whole items. The participants on the left side are marked with X. Each X represents 1.1 cases. The person ability scores ranged between plus two and minus two. A person with positive person ability answers with a higher probability those items correctly than someone with a negative one. Persons with a zero rank per definition were able with a fifty percent for a correct answer, altogether the person-separation-reliability scored 0.667. The test is suitable for separating our sample. On the right side, all items are distributed according to their difficulty. The item with the most positive value (item 16, see Figure 2) is the most difficult. The item with the highest negative value (item 11 and 23, Figure 2) is the easiest. The listing of our twenty-three questions displays three knowledge dimensions distributed according to their difficulty. The separation reliability of the items is 0.982. Every type had some difficult and some simple questions. The test appeared too easy since more questions were in the negative range. This is because in the Rasch model the post-tests should provide the calculation basis of the difficulties (Bond & Fox, 2010). The pre-test and the retention test would provide a falsified result: before the intervention, the probability is high that most of the questions were unanswered, resulting in a too difficult test. Similarly with the retention test, because after a few weeks, participants usually cannot remember everything.

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Figure 1: Schedule of questionnaire implementation

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Figure 2: Rasch Wright-map of all 23 knowledge-items

When analysing the difficulty index (number of right answers/ number of participants) of our questions, a threshold between 0.2 and 0.8 appeared (Bortz&Doering, 2006). Post-test mean scores of the questions of the three knowledge dimensions were: SYS: 0.52, SD = 0.15; ACT: 0.56, SD = 0.17; EFF: 0.53, SD = 0.18, meaning, e.g. that 53% of the pupils answered the system knowledge questions correctly. The distribution of our data via Kolmogorov-Smirnov calculation, corrected with Lilliefors, indicated a high significance (p < .001) for the knowledge responses; the Q-Q diagrams confirmed an absence of normality (Field, 2009). Hence, non-parametric tests were applied. The sum scores of correct answers were: pre-test: 8.62, SD = 3.03; post-test: 12.44, SD = 3.91; and in the retention test six weeks later: 11.87, SD = 3.73. Figure 2 shows the learning effect due to participation in our outreach module, the differences between the test times are shown. For calculating the differences between the three test times using the Wilcoxon-test, KN0 was used for the pre-knowledge, KN1 for the short-term acquired knowledge and KN2 for the retained knowledge. There were highly significant differences found: KN0-KN1: p < .001, Z = -17.48; KN0-KN2: p < .001, Z = -16.17; KN1-KN2: p < .001, Z = -4.93.A test/re-test group showed no significant difference (p = .875; Z = -.16; Mean score of KN0 = 7.93, SD = 2.73 and KN1 = 7.99, SD = 2.89), thus, no learning occurred just because of completing the questionnaires.

Subsequently, the differences between the three dimensions of environmental knowledge were analyzed: For all three dimensions of knowledge significant differences were found between the three test times: (1) SYS: KN0-KN1: p < 0.001; Z = -16.14; KN0-KN2: p < 0.001; Z = -14.22; KN1-KN2: p < 0.001; Z = -6.54; (2) ACT: KN0-KN1: p < 0.001; Z = -7.54; KN0-KN2: p < 0.001; Z = -6.06; KN1-KN2: p = 0.129; Z = -1.52; (3) EFF: KN0-KN1: p < 0.001; Z = -13.47; KN0-KN2: p < 0.001; Z = -13.82; KN1-KN2: p = 0.159; Z = -1.41. The mean sum scores were: (1) SYS: KN0 = 2.30 (SD = 1.28); KN1 = 4.20 (SD = 1.70); KN2 = 3.67 (SD = 1.46); (2) ACT: KN0 = 3.91 (SD = 1.51); KN1 = 4.52 (SD = 1.53); KN2 = 4.39 (SD = 1.59); (3) EFF: KN0 = 2.41 (SD = 1.56); KN1 = 3.72 (SD = 1.74); KN2 = 3.82 (SD = 1.82). The potential maximum of mean score would be twelve of system knowledge, eight of action-related knowledge and seven of effectiveness knowledge, just dependent on the number of questions for the three dimensions of knowledge.

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Figure 3: The mean sum score of the three knowledge dimensions of the three tests

All knowledge dimensions scored differently in all testing schedules when mean sum scores were compared pair-wise using the non-parametric Wilcoxon-test (Figure 2). For the pre-test: SYS-ACT (p< .001; Z = -14.94); SYS-EFF (p = .168; Z = -1.38); ACT-EFF (p< .001; Z = -13.503). For the post-test: SYS-ACT (p< .001; Z = -3.65); SYS-EFF (p < .001; Z = -5.39); ACT-EFF (p< .001; Z = -9.34). For the retention-test: SYS-ACT (

Finally, for correlating the corresponding pre-scores p< .001; Z = -8.22); SYS-EFF (p = .172; Z = -1.37); ACT-EFF (p< .001; Z = -6.37).-, post- and retention-scores of all three knowledge dimensions, due to multiple-testing, a Bonferroni correction was applied (which takes the number of mutually interdependent comparisons into account): in consequence, an alpha-value of 0.008/0.002/0.0002 was used for all correlations in the Figure . You can see, in Figure 4, significant Spearman-Rho correlations between the knowledge dimensions (every correlation in Figure 4 has a p-value smaller than 0.0002).

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Figure 4: Three-dimensional knowledge model with correlations

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Any monitoring of knowledge levels faces the basic problem of potentially inadequate psychometric foundation as such questionnaires consist of ad-hoc instruments originating in the taught contents. Therefore, quality assurances are essential. In our case, all applied measures showed high index levels, ranging from Rasch-modelling indicators to Cronbach’s alpha scores and difficulty indices. The Wright map (figure 1) showed normally distributed abilities and good separation. A separation of easy-to-answer and difficult-to-answer items apparently exist in all three knowledge types; additionally, the average difficulty indices between the three types of knowledge is quite similar, pointing to the positive features of our ad-hoc scale. Action-related knowledge items are only marginally easier than the system and effectiveness knowledge items. Therefore, the general cognitive learning outcome of our study is encouraging as participants were shown to acquire new knowledge (see Figure 2): While beforehand 8.6 out of 23 questions were correctly answered, immediately after the intervention, on average 12.4 out of 23 were answered correctly. Even six weeks later, participants were able to answer 11.9 correctly. Such scores demonstrate long-lasting cognitive learning effects produced by a short-term (half-day) outreach activity. This encouraging result is quite in line with other studies of the literature: For instance, Bogner (2002), Kaibel, Auwärter and Kravcik (2006), Fancovicova and Prokop (2011) or Fremerey and Bogner (2015) described significant knowledge increases due to short-term program participation. The positive effect on knowledge acquisition even after six weeks is noteworthy. In the literature, outreach and guided learning very often produces no large achievement scores, but assures long-lasting effects in cognitive learning: Meissner and Bogner (2011), for instance, described a hands-on inquiry-based intervention followed by a guided authentic tour in a salt mine as a successful approach to producing short-term cognitive achievement levels. Additionally, participants rated the lessons as interesting, important and useful and especially their well-being scored very high (Meissner & Bogner, 2011). Therefore, guided learning in line with hands-on elements and first-hand experiences is assumed to retain better scores even six weeks after an intervention compared to conventional teacher-centered learning units (Randler & Bogner). However, a successful learning unit must at least partially follow guidance, as Goldschmidt, Scharfenberg, and Bogner (2015) have shown that student-centered initiatives lead to lower cognitive achievement levels and require higher mental effort. Any high cognitive load (resulting in a high mental effort) may tend to handicap the cognitive achievement of an educational intervention: Scharfenberg and Bogner (2010, 2013a) had convincingly stressed the importance of pre-lab and interpretation phases within hands-on teaching approaches, which are supposedly the drivers to increase cognitive achievement and decrease mental effort. Scharfenberg and Bogner (2013b) also showed that the addition of tutors within learning groups did not produce automatically improved scores: Although continuous tutoring seemed to reduce the mental effort required, learners were more likely to approach tutors without investing additional effort simply in order to complete given tasks. More variables have been shown to influence educational programs: Fröhlich, Sellmann, and Bogner (2013) reported a strong link between newly acquired knowledge potential and situational emotions: interest and well-being always correlated highly positively‚, boredom negatively. Other background variables of interest are the environmental attitude sets preservation and utilization: Participants who are in favor of preservation tend to achieve better knowledge scores (Fremerey&Bogner, 2015; Schneller, Johnson &Bogner, 2013; Boeve-de Pauw& Van Petegem, 2011). Additionally, cognitive achievement influenced by short-term educational interventions appears to correlate strongly with invested effort and still, shows constancy even twelve weeks later (Schmid&Bogner 2015). Outreach interventions seem to successfully affect the long-term memories of participants.

In the pre-test, participants scored highest on action-related knowledge, while a lack of knowledge appeared in the dimensions of the system and effectiveness knowledge (see Figure 3). This result is partially in line with the literature: Frick et al. (2004) and Fremerey and Bogner (2014) reported higher levels of action-related knowledge compared to the other dimensions. The effectiveness knowledge level was observed to be lower than the level of the system knowledge. The distribution remained constant after participation, but differences dropped as the number of correct answers in all three types of knowledge became similar. In the retention-test, effectiveness and system knowledge were (statistically significantly different compared to the action-related knowledge, but the z-score was lower than in the pre-test. From pre-test to retention-test, the three types of knowledge reached similar levels. Immediately after the intervention, the most correct answers were found in the action-related knowledge scale and significantly fewer in the effectiveness knowledge scale. According to the literature, system knowledge, increase is often higher in comparison to the other types of knowledge (Fremerey&Bogner, 2014). However, this may simply be due to the marginal differences in the difficulty indices of our three items scales. Answering the action-related knowledge scale was apparently easier than the system knowledge scale, which may explain why ACT_KN1 in Figure 3 has the most correct answers compared to SYS_KN1 and EFF_KN1.

In comparison to the pre-test level, a significant increase overall dimensions of knowledge was measurable after this time. Pupils benefit from the educational outreach-learning program, although retention knowledge is limited, which corresponds to the literature: Normally distribution in knowledge acquisition shows a strong increase directly after the intervention and a smaller decrease after six weeks (Randler et al.; 2007; Randler&Bogner, 2002). System knowledge, six weeks after the intervention, showed a knowledge decrease. Action-related and specially effectiveness knowledge levels do not change.. The results are in line with Fremerey and Bogner (2014), where the decrease was smallest for effectiveness knowledge and highest for system knowledge. Pupils seem to acquire the three types of knowledge differently. Kaiser et al. (2008) showed that the system knowledge is the basis of the other two dimensions. System knowledge seems easier to be acquired as it consists of mere facts. Action-related knowledge often provides individuals with various options to act appropriately. Effectiveness knowledge deals with factual knowledge, which often does not seem to exist (Kaiser et al., 2008). Our results show the highest increase and then a slight drop in system knowledge: mere facts seem to be easy to learn in the short-term. As in other studies (Fremerey&Bogner, 2014, Frick et al., 2004), our pre-test scores point to previous high levels in action-related knowledge; consequently, due to ceiling effects, increasing that score is difficult. Action-related knowledge seems more available for pupils while effectiveness knowledge seems least prevalent. After a six week period, a decrease in effectiveness knowledge and action-related knowledge disappear. Pupils seem to acquire knowledge which is more effective for the environment and also for them. Our learning program apparently manages to impart all three types of knowledge and may equalize the levels of the three knowledge dimensions.

Relationships between the three types of knowledge Successful instructions in the three types of knowledge not only increase the knowledge level, but also strengthen the relationship between the individual dimensions (Kaiser et al., 2008). Our results are in line with the literature: Program participation had increased the relationships between the three types of knowledge. Correlations increased from pre- to post-test and decreased from post- to retention-test, but the reduction is smaller than its rise. Correlations in the pre-test demonstrated the initial situation. By applying the model of the three circles followingFremerey&Bogner (2014), every type of knowledge contributes to a circle (Figure 4). The three circles overlap only slightly because the correlation index r is quite low. In general, knowledge is only loosely linked before the intervention. However, relationships between the three types of knowledge exist in the pre-test as well, as Kaiser et al. (2008) postulated when hypothesizing the three knowledge-type model. Program participation increases all knowledge dimension levels, which thus correlate with each other. Knowledge circles become bigger and overlap more and more. Directly after the intervention, participants show the highest knowledge level, producing the highest relationships between the types of knowledge. Six weeks later, knowledge levels decreased again and knowledge circles become less overlap. The correlation declines, but it is higher than in pre-test. A further marginal effect is that the decrease between the relationship of action-related and effectiveness knowledge is the smallest. Both types of knowledge have lower forgetting rates.

Relationship between knowledge and individual behavior preferences Encouraging pro-environmental behavior is a frequent research issue (e.g., Vining &Ebreo 1990; McCarthy &Shrum 1994; Steg&Vleck 2009). The issue of recycling, in particular, seems to encourage theoretical disputes to define individual predictors of self-reported behavior (e.g., Ebreo, Hershey & Vining 1999; Ebreo& Vining 2001). Very often research seems to limit differences to demographic factors. For instance, females often are reported to score higher in environmental values (e.g. Bogner & Wiseman, 2006). This did not appear in our results, where no gender differences in general ecological behavior (GEB) were recorded. Nevertheless, reported behavior intentions correlate with pre-, post- and retention-knowledge. Pupils with a high score in the GEB know more about ecological themes before the intervention and because of this, they may have scored better values in pre-knowledge. Thus, the ecological behavior seems to provide some input to the individual learning potential Much research has reported an attitude-behavior gap in ecological behaviors. Research about the roles and impact of psychological distance in explaining sustainable and recycling behaviors seem to contribute to this, too (Schill& Shaw 2016): As a perspective to promote individuals towards behavioral action, facilitation and engagement in related behavior (which in our case is recycling and waste management) are promoted by “temporal, spatial, social and hypothetical closeness” to this behavior. Even more, as a relationship between GEB and knowledge existed even after six weeks, the individual knowledge may have reached deeper learning and maybe even networked and incorporated into the individual knowledge storages. Consequently, our educational program seems to have reached every participant, even those with low scores in GEB. In our outreach learning tour, everyone acquires knowledge, independently of interests in the topic.

Recommendations for further research and classroom implications

Any success of educational programs often is related to newly added knowledge levels. According to this view, our study successfully contributed knowledge by employing authentic guidance approaches as a meaningful approach to increasing cognitive achievement. Participants clearly had learned cognitively, despite the short duration of the intervention, and they retained this achievement for six weeks. This result is quite in line with other studies (e.g., Bogner, 1998; Sturm &Bogner, 2008; Fancovicova&Prokop, 2011). These results are quite encouraging as they successfully influence the system, action-related and effectiveness knowledge and show a substantial contribution to all three dimensions of knowledge. Especially encouraging is the long-lasting effect in the action-related and effectiveness knowledge dimensions. All three types of knowledge are regarded as important bases to influence general ecological behavior (GEB). Although changes in behavior were not anticipated in such short-term interventions, monitoring GEB at all testing times might provide insights to its interrelation with knowledge over time.

An authentic outreach tour after an introductory module clearly consolidates newly acquired knowledge. Especially first-hand experiences, which help participants to be prepared to extract as much as possible from an outreach environment, are often responsible for positive effects in individual ratings (Fremerey&Bogner, 2015; Johnson &Manoli, 2010). However, in view of the described benefits, one-day educational modules need integration into school curricula whenever possible. Currently, the waste issue is part of the 4th-grade syllabus, while political intentions currently appear in the grade-5 (“nature & technology”) with a focus on scientific work, in grade-7 (“physics and electricity”) and in grade-8 (“ecosystems under human influence”). Consequently, further modules are needed first to serve other age-groups which can harvest a growing understanding due to increasing syllabus complementarity, second to follow the advantages of spiral curricula where similar classroom was repeatedly taught, but with increasing complexity. Thus, our study can act as a solid basis, but not as a substitute. As the described lesson issue is an important one, a combination of classroom lessons with appropriate outreach experience will achieve long-term learning levels and thus meet the challenge to understand our lifestyles more appropriately – and convince us to modify them accordingly. That chance needs to be taken.

This study was supported in part by ZMS (ZweckverbandMüllverwertungSchwandorf [joint waste management authority]). We specifically thank G. Haverkamp for data collection and program implementation, F. Grabinger, S. Karl and F. Rupprecht for assistance throughout the study as well as all teachers and pupils for engaged participation in our study.

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Published: 06 April 2017

Reviewed By : Dr. Guilherme Frederico.Dr. Hong Jiang.Dr. Panagiotis Repoussis.

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