Auswahl der wissenschaftlichen Literatur zum Thema „P.A.O.K. (Soccer team)“

The aim of this study was to look into the alleged impact of the physical fitness performance of elite junior soccer players on the professional career according to playing position. The physical tests (sprint test, countermovement jump [CMJ], and maximal aerobic speed [MAS]) performed by 165 players enrolled in the junior team of a Spanish 1st Division club (1998-2010) were evaluated. Players were classified as those who were promoted to the reserve team (Reserve Team) and those who were not (non-Reserve Team), and as those who were promoted to the Spanish 1st/2nd Divisions (PFL) and those who were not (NPFL) until the 2020-2021 season. Overall, no significant differences (p>.05) were found between the Reserve Team and non-Reserve Team or between the PFL and NPFL players in neuromuscular performance. Aerobic fitness was greater (p<.05; ES = .79 ± .60 , moderate; mean difference = 3.0%) for the Reserve Team lateral midfielders in comparison to that for the non-Reserve Team. The results suggest that the impact of physical fitness on the promotion of elite junior soccer players is limited.

In soccer, different tactical behaviours of individuals are necessary to perform well as a team. The demands put on players from different positional roles can be manipulated in training through small-sided and conditioned games and tactical behaviours might be stimulated. The aim of this study was to assess the effects of positional role in tactical behaviour of U-17 youth soccer players based on core tactical principles in a four-a-side small-sided and conditioned game. The sample was comprised of 268 U-17 youth Brazilian soccer players. They were of five positional roles: centre backs; fullbacks; defensive midfielders; offensive midfielders; and forwards. The instrument used to collect and analyse data was the System of Tactical Assessment in Soccer (FUT-SAT). Results revealed that players of different positional roles showed no differences in the quantity of tactical actions performed. Moreover, forwards showed lower quality of tactical behaviour in the defensive phase (M=69.1, SD=16.0; p<.05) compared to fullbacks (M=77.0, SD=13.1; p<.05) and worse in actions far from the ball that ensured team cohesion in the defensive phase (M=69.7, SD=22.8; p<.05) compared to fullbacks (M=80.1, SD=18.4; p<.05). This study showed that the four-a-side format allows U-17 players to perform a similar quantity of tactical actions regardless of their positional role, but the player’s positional role influence quality of their tactical behaviour. Therefore, this structure might be used for trainings that promotes similar tasks and stimuli for players’ development that do not focus on specific tactical roles.

The purpose of this article was to investigate the influence of additional players and playing position on the network properties during 2x4 minutes small-sided and conditioned games (SSCG) in soccer. Eighteen young soccer players (age 16.4±0.7 years), six defenders, six midfielders, and six forwards, voluntarily participated in SSCGs with different task conditions (4vs.3, with an additional player inside the pitch, 3vs.3+2, with two support players at the side of the pitch, and 3vs.3, numerical equality). General (density, total links and clustering coefficient) and individual (degree centrality, degree prestige, and page rank) network properties were analyzed using the SocNetV® software. Results showed higher values of density (F=59.354, p=.001), total links (F=40.951, p=.001), and clustering coefficient (F=21.851, p=.001) during the 4vs.3 SSCG. Besides, midfielders showed higher values of degree centrality than defenders and forwards (F=10.669, p=.001). Midfielders and forwards also showed higher values of degree prestige than defenders (F=5.527, p=.005). These results indicate that both task condition and playing position influence the general and individual network properties during SSCGs. For this reason, it is suggested that both task condition and team composition need to be adjusted to the coaches’ purpose for each training session in order to maximize the possibilities of cooperation among the teammates.

Although it is assumed that athletes need to consider the member-to-member interactions that take place within a team before drawing an accurate perception about the team’s level of cohesion, little research to date has addressed this assumption. The purpose of this study was to examine the intrateam communication and cohesion relationship to determine which types of communication would be associated with perceived task and social cohesiveness in a sample of youth athletes. Youth soccer players ( N = 139, k = 13) completed measures of intrateam communication and task and social cohesion halfway through a competitive season. Separate multilevel analyses were run predicting task and social cohesion. For task cohesion, acceptance, positive conflict, and negative conflict communication emerged as significant predictors, p < .001, accounting for 40% of the total variance. For social cohesion, distinctiveness, positive conflict, and negative conflict communication were significant predictors, p < .001, accounting for 27% of the total variance. Findings provide initial evidence establishing a link between intrateam communication and cohesion in the youth sport context but more importantly suggest both similarities and differences with respect to the specific types of intrateam communication that are associated with task and social cohesion.

This study aims to evaluate the relation between salivary concentrations of cortisol (C), testosterone (T), the ratio T:C and the individual performance of top-level female soccer athletes during official matches. Eighteen female athletes from a national soccer team (age 23.06 ± 4.33 years) participated in the study. Four official matches were analysed and the on-field time of each player as well as the index of individual effectiveness were calculated. Players were classified in two clusters according to their individual performance (cluster 1 – poor individual performance; cluster 2 – good individual performance) using K-means and their hormonal variables were compared. The players of cluster 2 generally revealed higher values (p˂.05) in both the positive actions and individual effectiveness, when compared with the players of cluster 1. The players of both clusters presented identical values of C, T and T:C at the four evaluated matches. The athletes of cluster 2 showed a significant increase in C (p˂.05) and a significant decrease in T and T:C before the games lost (M2 and M5). However, there was no linear relation between the variation of both hormones during the matches and the individual performance of players. Performance in competition revealed significant differences between players, though with no apparent relation to the hormonal kinetics of C and T before and after the matches.

Abstract Speed, power and agility are important components of fitness and determine the level of success and performance in soccer. The aim of this study was to identify speed variables and to determine their mutual correlation and structure in youth elite soccer players. The research group consisted of players from the Czech U16 national team (n = 22, age = 15.6 ± 0.4 years). Speed variables were assessed using the following tests: a) linear speed: 5 m sprint (S5), 10 m sprint (S10) and 20 m flying sprint (F20); b) the agility: agility test 505 with turning on the dominant (A505D) and nondominant legs (A505N) and the K-test (K) and c) ball velocity after an instep kick with the dominant (IKD) and nondominant (IKN) legs. Significant dependence was found for S5 compared with S10, F20 vs. A505N, K vs. A505N (p < 0.01) and S10 vs. F20 (p < 0.05). The factor analysis revealed three components of the latent variable - speed. The first component consisted of linear sprint (S10, S20) and also partially consisted of maximum speed (F20). The second component was primarily composed of agility (A505D, A505N, K) and also included maximum speed (F20). The third independent component represented ball velocity after an instep kick (IKD, IKN). The speed variables in youth elite players exhibited significant heterogeneity from the perspective of performance, as determined by the monitored tests. The structure of the speed predisposition indicated that there were three components of speed. The results of our studies support the notion that each component of speed must be considered independently when designing training programmes.

Research within sport science disciplines seeks to enhance performance via the combination of factors that influences the team’s periodization. The current study aimed to investigate the variations in training load (TL), and the consequential changes in fitness variables, based on the use of match difficulty prediction model (MDP), level of opposition (LOP), days between matches, and match location during 12 weeks in the competitive period I. Seventeen elite soccer players (age = 17.57 ± 0.49 years; body height 1.79 ± 0.05 m; body weight 72.21 ± 6.96 kg), have completed a Yo-Yo intermittent recovery test, a running-based anaerobic sprint test, a soccer-specific repeated sprint ability, and a vertical jump test to identify changes in players fitness. TL was determined by multiplying the RPE of the session by its duration in minutes (s-RPE). Training monotony, strain, and acute:chronic workload ratio (ACWR) were also assessed. A simple regression model was conducted and the highest variances explained (R2) were used. The LOP score explained most of the variance in ACWR (r= 0.606, R2=0.37). TL declined significantly when compared the match-day by the first three days and the last three days of the week. No significant difference was found in s-RPE between the high and low MDP factor. Strong negative correlations were reported between ACWR and LOP (r=-0.714, p&lt;.01). In addition, we found a significant improvement in repeated sprint ability, aerobic and anaerobic fitness variables between pre- and post-test in fatigue index (d=1.104), best testing time, ideal time, total time and mean-best (d=0.518-0.550), and aerobic and anaerobic fitness variables (p&lt;.05), respectively. The MDP could facilitate the training prescription as well as the distribution of training intensities with high specificity, providing a long-term youth player’s development and allowing teams to maintain optimal fitness leading into more difficult matches.

Purpose:A review of published studies monitoring sprint performance reveals considerable variation in start distance behind the initial timing gate. The aim of the current study was to generate correction factors across varying flying-start distances used in sprint testing with photocells.Methods:Forty-four well-trained junior soccer players (age 18.2 ± 1.0 y, height 175 ± 8 cm, body mass 68.4 ± 8.9 kg) performed sprint testing on an indoor sprint track. They were allocated to 3 groups based on sprintperformance level. Times for 10- and 200-m sprint with foot placement ranging from 0.5 to 15 m back from the initial timing gate were recorded twice for each athlete.Results:Correction-factor equation coefficients were generated for each of the 3 analyzed groups derived from the phase-decay equation y = (y0 − PL) × exp(−k × x) + PL, where y = time difference (0.5-m flying start as reference), x = flying-start distance, y0 is the y value when time is zero, PL (plateau) is the y value at infinite times, and k is the rate constant, expressed in reciprocal of the x-axis time units; if x is in seconds, then k is expressed in inverse seconds. R2 was ≥.998 across all athlete groups and sprint distances, demonstrating excellent goodness of fit. Within-group time differences were significant (P < .05) across all flying-start distance checkpoints for all groups. Between-groups time-saving differences up to 0.04 s were observed between the fastest and the slowest groups (P < .05).Conclusions:Small changes in flying-start distances can cause time differences larger than the typical gains made from specific training, or even the difference between the fastest and slowest elite team-sport athletes. The presented correction factors should facilitate more meaningful comparisons of published sprint-performance results.

Introduction Systematic assessment of load and recovery in athletes is essential for effectively adjusting various training demands and their corresponding recovery measures (Kellmann et al., 2018), thereby reducing the risk of nonfunctional overreaching, overtraining, and potential subsequent injuries and illnesses (Bourdon et al., 2017; Kellmann et al., 2018; Taylor et al., 2012). The information obtained from the assessment can support athletes, coaching staff, and their medical teams in the tightrope act between performance optimisation and injury risk reduction. The expert consensus in the field of load and recovery monitoring and other research emphasises the importance of employing a multivariate approach for assessing load and recovery (Bourdon et al., 2017; Kellmann et al., 2018). Various physiological and psychological measures should be used for this purpose (Heidari et al., 2019). In team sports, it is also required that these assessments be carried out quickly, non-invasively, and with minimal added burden on the athletes (Thorpe et al., 2017). In this research project, we developed a web application-based Load and Recovery Score (LRS) and evaluated its relationship with established load parameters. It is assumed that specific training and match load variables correlate negatively with the following day’s LRS when controlled for intra-subject variability. Methods 78 female and male athletes from the U18, U19 and U21 teams of the Swiss soccer club “BSC Young Boys” were selectively recruited. 71 players (32.4% female) with an average age of 17.9 years (SD = 1.2) were monitored over a minimum period of 35 days. A repeated-measure design by means of a five-to-seven-week prospective longitudinal data collection was used in this study. The dependent variable (LRS) and four other independent load variables were repeatedly measured over time in the same athletes. The LRS comprises eight subscales integrated into an interval-scaled score ranging from 0 to 120. A higher score indicates a better recovery state and lower loads. The players recorded values for these eight different subscales daily using the web application. The subscales include questions drawn from various previously validated questionnaires related to the player’s 1) Physical capability, 2) General state of regeneration, 3) Muscular stress, 4) Fatigue, 5) Mood, and 6) Sleep quality, contributing to the recovery component of the score. Additionally, there are two load subscales pertaining to the player’s 7) Heart Rate Variability (HRV) and their 8) Acute:Chronic Workload Ratio (ACWR). The entries are either directly recorded on an ordinal scale (0-6) or are converted to conform to this scale level. Daily logs are incorporated into the different subscale values using a specific algorithm. The algorithm is informed by current research recommendations and is a proprietary business secret. The independent variables included the subjective Player- and Trainer – Session Rating of Perceived Exertion (PSRPE/TSRPE), as well as two GPS and accelerometry-based parameters: Total distance covered (TD) and Total distance > 20km/h (TD20). To examine direction and strength of the relationship between the LRS and the above-mentioned measures of training and match load, various linear mixed-effects models (LMM) were fitted via restricted maximum likelihood (REML). Random intercepts were defined for each player to account for the repeated within-subject measurements (Fisher et al., 2018; Molenaar & Campbell, 2009; Neumann et al., 2021), and the demographic control variables Height, Body mass and Sex were included in the models. Furthermore, the variance explained by the random effects was calculated using Nakagawa’s marginal and conditional R2 for mixed models. Results All training and match load parameters demonstrated significant negative correlations with the subsequent day’s LRS. In the linear mixed-effects model analysis PSRPE and TSRPE showed similar fixed effects (-0.013, 95% CI [-0.017, -0.010], p < .001 versus -0.008, 95% CI [-0.011, -0.006], p < .001), while TD exhibited stronger associations (-0.668, 95% CI [-0.979, -0.355], p < .001) than TD20 (-0.009, 95% CI [-0.012, -0.006], p < .001). The addition of control variables did not significantly influence direction or magnitude of the model’s effects. Variance explained by the residual factor ID (defining each individual) was high (≥ 0.444) in all of the analyses and post-hoc analyses on the influence of the variables Playing position and Sex showed high variation between these subgroups. Discussion/Conclusion The results show that the LRS has significant negative associations when controlled for repeated within-subject measurements with different subjective and objective training and match load measures, such as the PSRPE, the TSRPE, TD, and TD20. Therefore, it can track the effect of those variables whilst also being an indicator of different recovery parameters. All training and match load variables behave according to the a priori assumption and correlate negatively with the following day’s LRS. This is in line with the available literature, where it has already been shown that certain parameters, which are also part of the score, show good moderate to strong evidence for associations with different load indicators. The fact that the variance explained by the residual factor ID and the influence of grouping variables (Playing position/Sex) was high in all the analyses is consistent with current research (Hader et al., 2019; Neumann et al., 2021), where the impact of the different load parameters on recovery varied across groups and individuals. No single marker can provide global information (Temm et al., 2022) regarding an athlete’s recovery. The comprehensive LRS offers a solution to that problem because it can track different load parameters in elite youth soccer players and present multiple accepted recovery and load measures separately and on an individual level so that athletes, coaches and staff can use it to enhance their knowledge of responses (Bourdon et al., 2017) and determine future training and match load as well as suited means of recovery. By doing this, injury risk could be reduced and performance optimised. The ultimate decision of which monitoring tools to work with should remain with the sports professionals. It is essential that the protocol has reasonable practicability and uses an individualised (Temm et al., 2022), and multimodal approach, including biological and social aspects (Heidari et al., 2019). References Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., Gabbett, T. J., Coutts, A. J., Burgess, D. J., Gregson, W., & Cable, N. T. (2017). Monitoring athlete training loads: Consensus statement. International Journal of Sports Physiology and Performance, 12(Suppl 2), S2161–S2170. https://doi.org/10.1123/IJSPP.2017-0208 Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences, 115(27), E6106–E6115. https://doi.org/10.1073/pnas.1711978115 Hader, K., Rumpf, M. C., Hertzog, M., Kilduff, L. P., Girard, O., & Silva, J. R. (2019). Monitoring the athlete match response: Can external load variables predict post-match acute and residual fatigue in soccer? A systematic review with meta-analysis. Sports Medicine - Open, 5(1), Article 48. https://doi.org/10.1186/s40798-019-0219-7 Heidari, J., Beckmann, J., Bertollo, M., Brink, M., Kallus, W., Robazza, C., & Kellmann, M. (2019). Multidimensional monitoring of recovery status and implications for performance. International Journal of Sports Physiology and Performance, 14(1), 2-8. https://doi.org/10.1123/ijspp.2017-0669 Kellmann, M., Bertollo, M., Bosquet, L., Brink, M., Coutts, A. J., Duffield, R., Erlacher, D., Halson, S. L., Hecksteden, A., Heidari, J., Kallus, K. W., Meeusen, R., Mujika, I., Robazza, C., Skorski, S., Venter, R., & Beckmann, J. (2018). Recovery and performance in sport: Consensus statement. International Journal of Sports Physiology and Performance, 13(2), 240–245. https://doi.org/10.1123/ijspp.2017-0759 Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x Neumann, N. D., Van Yperen, N. W., Brauers, J. J., Frencken, W., Brink, M. S., Lemmink, K. A. P. M., Meerhoff, L. A., & Den Hartigh, R. J. R. (2021). Nonergodicity in load and recovery: Group results do not generalize to individuals. International Journal of Sports Physiology and Performance, 17(3), 391–399. https://doi.org/10.1123/ijspp.2021-0126 Taylor, K.-L., Chapman, D., Cronin, J., Newton, M., & Gill, N. (2012). Fatigue monitoring in high performance sport: A survey of current trends. Journal of Australian Strength and Conditioning, 20, 12–23. Temm, D. A., Standing, R. J., & Best, R. (2022). Training, wellbeing and recovery load monitoring in female youth athletes. International Journal of Environmental Research and Public Health, 19(18), 11463. https://doi.org/10.3390/ijerph191811463 Thorpe, R. T., Atkinson, G., Drust, B., & Gregson, W. (2017). Monitoring fatigue status in elite team-sport athletes: Implications for practice. International Journal of Sports Physiology and Performance, 12(Suppl 2), S227–S234. https://doi.org/10.1123/ijspp.2016-0434

The aim of the present study was to compare the fitness profiles and internal training loads between senior team and academy team soccer players during an in-season phase. Twenty-two professional soccer players from the senior team (n=12; 28.3<img width="12" alt="" height="20"> 2.0 years) and under 19 (U19) team (n=10; 18.0<img width="12" alt="" height="20"> 0.4 years) of the same club participated in the present study. High-intensity running performance, acceleration, maximal sprint, and change of direction (COD) ability were all tested during the mid-season break of a competitive season. Session rating of perceived exertion (sRPE) reflecting the internal training load during the entire first half of the season was being documented daily. Senior players showed small to moderate superiority in COD (1.8%, 90% confidence intervals [CI, -3.2; 7.1], ES: 0.24 [-0.44; 0.92]), maximal sprint (2.3%, [0.0; 4.7], ES: 0.81 [0.00; 1.63]) and acceleration (3%, [0.2; 5.8], ES: 0.96 [0.06; 1.85]). The U19 showed small better high-intensity intermittent running fitness (2.5%, [-1.2; 6.3], ES: 0.39 [-0.20; 0.97]). When analyzing internal training loads (from M-3 to M+3), the U19 showed small to very large higher sRPE values for all days (range; 8.2%; 229.3%, [-8.1; 328.3], ES range; 0.25; 2.70, [-0.26; 3.3]), except for match days (M), on which unclear trivial difference was observed (-1.5%, [-9.6; 7.5], ES -0.09 [-0.65; 0.46]). Our results showed that senior players and youth players had different fitness profiles and internal training loads during the first half of a competitive season; this should be taken into consideration when designing specific and individualized recovery and training sessions.

Nursing professionals need to assume responsibility and take initiative in ongoing personal and professional development. Qualities required of nursing graduates must include the ability to, “translate, integrate, and apply knowledge that leads to improvements in patient outcomes,” in an environment in which “[k]nowledge is increasingly complex and evolving rapidly” (American Association of Colleges of Nursing, 2008, p. 33). The ability to identify personal learning needs, set goals, apply learning strategies, pursue resources, and evaluate outcomes are essential. Nursing professionals must be self-directed learners to meet these expectations. Team-based learning (TBL) is a multiphase pedagogical approach requiring active student participation and collaboration. Team-based learning entails three stages: (1) individual preparation, (2) learning assurance assessment, and (3) team application activity.

This chapter documents findings from the Making CT (Computational Thinking) project, a collaborative effort between project team members and elementary teachers that aims to reimagine interdisciplinary, computational thinking-infused making lessons for a virtual format. Virtual making CT lessons were grounded in four design principles: standards-based practices, clear and explicit expectations, multiple means of engagement, and opportunities for collaboration. Drawing on data from virtual teacher professional development sessions, lesson implementation, and teacher interviews, this chapter illustrates how teachers were able to engage in the difficult work of reconceptualizing CT-infused making lessons for the virtual classroom. These principles can be used to support the design of other interdisciplinary activities to support P-5 students' development of creative and authentic problem-solving in virtual learning environments.

Abstract The original MIT India Project team consisted of only three members: George Rosen, who was already working closely with the Reserve Bank in Bombay, and Trevor Swan and myself, who were established with an office in New Delhi. Our terms of reference agreed with the Government (that is, the Planning Commission) were very vague, but I think we were supposed to galvanize empir¬ical economic research in liaison with four existing institutions: the National Council of Applied Economic Research under Dr Lokanathan, the Economic Growth Centre of the Delhi School of Economics under V. K. R. V. Rao (both in Delhi), the Indian Statistical Institute in Calcutta under P. C. Mahalanobis, and the Gokhale Institute in Poona under Dr Gadgil. These terms of reference were totally unrealistic for reasons of time, distance, personality, and lack of any research material. Outside government there were almost no up-to-date figures for anything, and we had neither the time (Swan and I were in India for only nine months) nor the resources to create data. Nor was this what we were sup¬ posed to do.

<p>TQL Stadium is the first dedicated home for FC Cincinnati, a major league soccer team. Buro Happold provided structural engineering services for the stadium. The stadium design meets all Major League Soccer criteria as well as being able to host FIFA sponsored events. All 26,000 seats in the newly designed stadium have excellent sightlines. A complete, 360-degree canopy roof covers every single seat in the stadium. Particular special characteristics of the structural design included the many varieties of seating and hospitality arrangement, the fast schedule and the structural façade interaction. Additionally the paper describes aspects of the analysis for seismic loading.</p>

Global popularity of football (soccer) has led to implementation of scientific and technolog-ical knowledge in its everyday use. One of such things that has been expanding in recent years is the application of various technologies for monitoring running performance during trainings and matches. The aim of this study was to evaluate match running performance of professional football/soccer players during oficial matches by using global positioning sys-tem (GPS) technology, and to compare it among playing positions. One hundred and one match performance of “Hajduk” team in 14 matches of Croatian Football League season 2018/2019 were used for this study. The activities of the players were monitored using GPS technology (Catapult S5 and X4 devices, Melbourne, Australia) with a sampling frequency of 10 Hz. Total distance covered, distance in different speed cat-egories, total and high intensity accelerations and decelerations were analyzed for players in five different playing positions: central defenders (n=26), full-backs (n=24), midfielders (n=33), wingers (n=10), and forwards (n=8). Additionally, running performances were cor-related with InStat index, regular performance indicator which is calculated on the basis of unique set of key parameters for each position (12 to 14 factors). Average total distance covered during match was 10.3 km, with midfielders covering larg-est (11.1 km) and central backs covering smallest average distance (9.3 km). Playing po-sitions differed significantly in high intensity running (F-test = 21.97 and 18.84, p 25 km/h, respectively). The side positions (wingers and full-backs) covered highest-, while central defenders covered lowest-average distance (914, 775, and 376 m, respectively). The wingers had highest number of high intensity accelerations and decelerations (> 3 m/s2; F-test = 16.56 and 17.98, p 0.5 m/s2; F-test = 6.57, and 15.26, p < 0.01, respectively). InStat index was not correlated with data ob-tained by GPS measurement. Results from this study indicate that running demands differ depending on playing positions so these findings should be applied in creating training plan and program. Future studies should evaluate data from multiple teams for getting more applicable findings.

Introduction There are steadily expanding claims that teacher community contributes to the improvement in the practices of teaching and schooling (cf., Witziers et al., 1999; Little, 2003; Darling-Hammond and Bransford, 2005) as well as individual teacher development and the collective capacity schools (cf., Seashore Louis et al., 1996; Grossman et al., 2001; Imants et al., 2001; Achinstein, 2002; Piazza et al., 2009). In line with Grossman et al. (2001), we are interested in teacher community at the local level, where interaction, dialogue and trust are necessary elements of building cohesion. Based on the definition of community by Bellah et al (1985), we define a teacher community as ‘a group of teachers who are socially interdependent, who participate together in discussion and decision making, and share and build knowledge with a group identity, shared domain and goals, and shared interactional repertoire’. This means that we distinguish three core features of a teacher community: group identity, shared domain and goals, and shared interactional repertoire. These features refers to the nature of a community (group identity), what a community is about (shared domain), and how it functions (shared interactional repertoire). In a literature review of Brouwer et al. (in press), 31 design principles have been retrieved from the literature about the setup of efficient and effective teacher communities in schools. Examples of design principles are the promotion of interdependence, shared responsibility and individual accountability, the development of guidelines for dealing with conflicts and decision-making, and the consideration of group size and heterogeneity of expertise. The use of online workspaces might solve issues in communication and collaboration of school teachers as well as in establishing feelings of cohesion and trust –in addition to face-to-face interaction and collaboration. However, the problem is that we do not know how online workspace should be designed in order to efficiently and effectively communities of teachers in secondary school. Method and results A systematic review will be presented of online workspaces from the perspective of how teacher communities should be designed in order to effectively and efficiently support collaboration and communication of teachers in secondary schools. These tools includes tools for collaborative writing, file sharing, mind mapping, group communication, social networking, wikis and blogs, web presenting, whiteboarding, web and video conferencing, chat and instant messaging, and project management and event scheduling. Subsequently, online collaboration tools are evaluated on the way their functionalities potentially facilitate the design principles that have been worked out. Literature Achinstein, B. (2002), “Conflict amid community: The micropolitics of teacher collaboration”, Teacher College Record, Vol.104 No.3, pp.421-455. Bellah, R. N., Madsen, N., Sullivan, W. M., Swidler, A., & Tipton, S. M. (1985). Habits of the heart; Individualism and commitment in American life. Berkeley, CA: University of Calidofornia Press. Brouwer, P., Brekelmans, M., Nieuwenhuis, L., & Simons, P. R. J. (in press). Fostering teacher community development A review of design principles and a case study of an innovative interdisciplinary team. Learning Environments Research. Darling-Hammond, L. and Bransford, J. (Eds.) (2005), Preparing teachers for a changing world. What teachers should learn and be able to do, Jossey-Bass, San Francisco. Grossman, P., Wineburg, S., & Woolworth, S. (2001). Toward a theory of teacher community. Teacher College Record, 103, 942-1012. Imants, J., Sleegers, P. and Witziers, B. (2001), “The tension between sub-structures in secondary schools and educational reform”, School Leadership & Management, Vol.21, No.3, pp.289-307. Little, J. W. (2003), “Inside teacher community: representations of classroom practice”, Teachers College Record, Vol.105 No.6, pp.913-945. Piazza, P., McNeill, K.L. and Hittinger, J. (2009), “Developing a voluntary teacher community: The role of professional development, collaborative learning and conflict”, Paper presented at the annual meeting of the American Educational Research Association, April, San Diego, CA. Seashore Louis, K., Marks, H. and Kruse, S. (1996), “Teachers’ professional community in restructuring schools.” American Educational Research Journal, Vol.33 No.4, pp.757-798. Witziers, B., Sleegers, P. and Imants, J. (1999), “Departments as teams: functioning, variations and alternatives”, School Leadership & Management, Vol.19 No.3, pp.293- 304.