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Modelling diffusion of a personalized learning framework

  • Special Issue on Personalized Learning
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Abstract

A new modelling approach for diffusion of personalized learning as an educational process innovation in social group comprising adopter-teachers is proposed. An empirical analysis regarding the perception of 261 adopter-teachers from 18 schools in India about a particular personalized learning framework has been made. Based on this analysis, teacher training (TT) has been identified as one of the dominant factor which can significantly influence decision by teachers to adopt the educational innovation. Different situations corresponding to fixed and time dependent dynamic carrying capacity of potential adopter-teachers at any time have been developed. New generalized models capturing the growth dynamics of the innovation diffusion process in conjunction with the evolutionary carrying capacity of potential adopters are investigated. The coupled dynamics allows forecasting the likelihood of success or failure of new educational innovation in a given context. Different scenarios for TT are considered based on—constant growth rate model; proportional growth rate model; stratified growth rate model. The proposed modelling framework would be of great interest to education policy makers as it has the potential to predict the likelihood of success or failure of new educational innovation.

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References

  • Anderson, S. E. (1997). Understanding teacher change: Revisiting the concerns based adoption model. The Ontario Institute of Studies in Education, Curriculum Enquiry 27:3.

    Google Scholar 

  • Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15, 215–227.

    Article  Google Scholar 

  • Bennett, R. (2001). Lecturers’ attitudes towards new teaching methods. The International Journal of Management Education (LTSN Business, Management and Accountancy Centre), 2(1), 42–58.

    Google Scholar 

  • Bentley, T., & R. Miller. (2004). Personalisation; creating the ingredients for systematic and society-wide change. A paper presented in Personalised Learning Conference, London.

  • Brandsford, J., Brown A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.

  • Carlson, R. O. (1968). Summary and critique of educational diffusion research. In Research implications for educational diffusion: Major papers presented at the National Conference on Diffusion of Educational Ideas (pp. 4–23), East Lansing, Michigan: Michigan State University.

  • Clark, C. M., & Peterson, P. L. (1986). Teachers’ thought processes. In: M. C. Witt rock (Ed.), Handbook of research on teaching. New York: Macmillan.

  • Cohen, D. K., & Ball, D. L. (1990). Policy and practice: An overview. Educational Evaluation and Policy Analysis, 12(3), 347–353.

    Google Scholar 

  • Cronbach, L., Snow, R. (1977). Aptitudes and instructional methods: A handbook of research on interactions. New York: Inrington Publishers.

  • Emrick, J. A., & Peterson, S. M. (1977). Evaluation of the National Diffusion Network. Final report. Volume II: Technical supplement. Menlo, California: Stanford Research Institute.

  • Fang, Z. (1996). A review of research on teacher beliefs and practices. Educational Research, 38(1), 47–65.

    Article  Google Scholar 

  • Gess-Newsome, J., Southerland, S. A., Johnston, A., & Woodbury, S. (2003). Educational reform, personal practical THEORIES, and dissatisfaction: The anatomy of change in college science teaching. American Educational Research Journal, 40(3), 731–767.

    Article  Google Scholar 

  • Hall, G. E., & Hord, S. M. (1987). Change in schools facilitating the process. New York, NY: State University of New York Press.

    Google Scholar 

  • Hall, G. E., Wallace, R. C., & Dossett, W. A. (1973). A developmental conceptualization of the adoption process within educational institutions (Rep. No. 3006). Austin, Texas: The University of Texas at Austin, The Research and Development Center for Teacher Education.

  • Helsel, A. R. (1972). Teachers’ Acceptance of Innovation and Innovation Characteristics. The High School Journal, Vol. 56, No. 2, Accountability and Related Ideas.

  • Horizon Report, New Media Consortium (2011). http://www.nmc.org/publications/2011-horizon-report. Retrieved Oct 2011.

  • Karmeshu, D. G. (2001). Stochastic evolution of innovation diffusion in heterogeneous groups: Study of life cycle patterns. IMA Journal of Management Mathematics, 12, 107–126.

    Article  Google Scholar 

  • Karmeshu, & Goswami, D. (2008). Transient bimodality and catastrophic jumps in innovation diffusion. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 38(3), 644–654.

    Article  Google Scholar 

  • Karmeshu, & Pathria, R. K. (1980). Stochastic evolution of a nonlinear model of diffusion of information. Journal of Mathematical Sociology, 7, 59–71.

    Article  Google Scholar 

  • Pajares, M. (1992). Teacher’s beliefs and educational research: Cleaning up a messy construct. Review of Educational Research, 62(3), 307–332.

    Google Scholar 

  • Raman, R., Nedungadi, P. (2011). Transitioning to ICT enabled continuous and comprehensive evaluation—reducing teacher workload: Proceedings of the 3rd international conference on machine learning and computing, Singapore.

  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: The Free Press.

    Google Scholar 

  • Rogers, E. M., & Jain, N. C. (1968). Needed research on diffusion within educational organizations.

  • Vanden Bulte, C., & Joshi, Y. (2007). New product diffusion with influentials and imitators. Marketing Science, 26(3), 400–421.

    Article  Google Scholar 

  • Watson, D. M. (1998). Blame the techno centric artefact! What research tells us about problems inhibiting teacher use of IT? In G. Marshall, & M. Ruohonen (Eds.), Capacity building for IT in education in developing countries (pp. 185–192). London: Chapman & Hall.

Download references

Acknowledgements

The authors would like to thank reviewers whose comments have led to considerable improvement of the quality of the paper, Mr. V. P. Jain for extensive discussions and suggestions about the modelling framework, Mr. Sudheer Sharma for his help with numerical simulations and Dr. V. B. Lal for his thoughtful comments and observations.

Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

    Karmeshu

  2. School of Engineering, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

    Raghu Raman & Prema Nedungadi

Authors
  1. Karmeshu
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  2. Raghu Raman
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  3. Prema Nedungadi
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Corresponding author

Correspondence to Karmeshu.

Appendix

Appendix

Factors affecting teacher adoption of CCE personalized learning framework

  • TT

    • TT refers to the extent to which individuals consider the amount and type of training they received to be useful in using the CCE.

  • TI

    • TI refer to the degree to which individuals perceive school management as providing incentives to encourage CCE implementation. If teachers expect to be rewarded extrinsically, for example, by receiving a bonus or being commended for their achievements, then they will have greater willingness to adopt CCE.

  • TW

    • TW represents the perception of the time and effort required to integrate CCE in classroom teaching.

  • PE

    • Interpersonal influence appears to be extremely important in influencing potential adopters, as is demonstrated by the fact that the opinions of peers significantly affect the way in which an individual feels pressures associated with adoption of the innovation. On the otherhand, while some teachers oppose the adoption of CCE, other teachers may be influenced by them.

  • PU

    • Is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance”. Therefore, teachers will be more willing to adopt CCE if adoption can help them achieve better teaching outcomes.

  • PE

    • PE is defined as “the degree to which a person believes that using a new innovation would be free of effort”. When users find an innovation is hard to use, the acceptance and usage rate would be influenced in a negative way. Therefore, if CCE is easy-to-use, teacher adoption intention is enhanced

  • C

    • C is defined as the degree to which an innovation is perceived as consistent with the existing values, needs and past experiences of potential adopters. If CCE makes a teacher feel they are using something they are somewhat familiar with and which meets their needs and habits, they adopt CCE in their teaching.

Survey questionnaire with item mean scores

Survey options: strongly disagree (1); disagree (2); neither agrees nor disagree (3); agree (4); strongly agree (5)

TT

Item mean scores

Training sessions provided were very useful

4.9

Training materials provided were very informative

4.8

Training materials provided are effective in expressing the objectives of CCE

4.9

Website for CCE provides information in a variety of ways (text, graphic, animation, audio, video, etc.)

4.8

My school does not provide convenient time for getting trained on CCE

4.5

Average factor score

4.8

TI

 My willingness to adopt CCE would be influenced by the rewards the school provides

3.6

 My willingness to adopt CCE would be influenced if the school considers using CCE as an item in the teacher performance evaluation

3.3

 The school would timely reward the teachers who have adopted CCE

3.1

 Average factor score

3.3

TW

 I don’t have the time to enter data CCE data

4.1

 The effort is high for me to enter CCE data

4.2

 I am worried that if give feedback to students, I will have to spend additional time answering follow up questions

3.4

 Average factor score

3.9

PI

 My teacher colleagues think that using CCE is valuable for teaching

2.8

 My teacher colleagues’ opinions are important to me

2.8

 If most of my colleagues have started to use CCE to support their teaching, this fact would press me to do the same

3.2

 I will learn how to use CCE after seeing my teacher colleagues use it

2.4

 Average factor score

2.8

SS

 The school is committed to implementing CCE

4.2

 The school is committed to supporting my efforts in using CCE for teaching

3.1

 The school strongly encourages the use of CCE by teachers

3.6

 The school will recognize my efforts in using CCE for teaching

3.1

 The use of CCE for teaching is important to the school

3.3

 I have pressures from my organization to use CCE

2.1

 Average factor score

3.2

PU

 Using CCE enables me to accomplish my teaching tasks more quickly

2.8

 Using CCE improves the quality of my teaching

3.2

 Using CCE makes teaching easier

2.7

 Using CCE enhances my teaching effectiveness.

3.1

 Using CCE gives me greater control over my teaching

3.4

 CCE supports student learning in different ways

3.6

 Using CCE would improve my performance in teaching (save time and effort)

2.1

 Using CCE would improve by productivity

2.4

 Average factor score

2.9

PE

 Using CCE to support my teaching is clear and understandable

2.6

 When using CCE to support my teaching, it is easy to get the software tools to support CCE

2.1

 Overall, I believe that it is easy to use CCE to support my teaching

2.2

 Average factor score

2.3

C

 Using CCE will fit well with the way I teach and assess students

2.8

 Using CCE will fit into my style of teaching

2.4

 I feel already over-burdened without adding CCE into my instructional process

3.8

 Average factor score

3.0

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Karmeshu, Raman, R. & Nedungadi, P. Modelling diffusion of a personalized learning framework. Education Tech Research Dev 60, 585–600 (2012). https://doi.org/10.1007/s11423-012-9249-2

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