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Dr Richard Noble's Outputs (4)

Towards a holistic indicator of flow disturbance impact: fish community size spectra (2016)
Presentation / Conference Contribution
Alonso Gonzalez, C., Herranz, J., Solana Gutierrez, J., Garcia De Jalon Lastra, D., & Noble, R. (2016, February). Towards a holistic indicator of flow disturbance impact: fish community size spectra. Presented at 11th International Symposium on Ecohydraulics, Melbourne, Australia

Fish size-spectra provide a simple synthetic metric to assess the effects of pressures on the overall function of river ecosystems. An exploration of the effects of hydrological pressures on this metric is addressed in order to test its sensitiveness... Read More about Towards a holistic indicator of flow disturbance impact: fish community size spectra.

Prioritising eel-migration barriers for passage improvements (2011)
Presentation / Conference Contribution
Noble, R., Nunn, A., Harvey, J., & Cowx, I. (2009, April). Prioritising eel-migration barriers for passage improvements. Presented at Institute of Fisheries Management 2009 Eel Conference, Bridgwater, Somerset

Physical barriers are becoming increasingly recognised as major factors influencing the migrations, population structures, spawning success and recruitment of freshwater organisms. A high-profile example is that of the European eel, which has recentl... Read More about Prioritising eel-migration barriers for passage improvements.

Use of the dynalearn learning environment by naïve student modelers: Implications for automated support (2011)
Presentation / Conference Contribution
Noble, R., & Bredeweg, B. Use of the dynalearn learning environment by naïve student modelers: Implications for automated support

This paper shows that naïve students will require coaching to overcome the difficulties they face in identifying the important concepts to be modeled, and understanding the causal meta-vocabulary needed for conceptual models. The results of this stud... Read More about Use of the dynalearn learning environment by naïve student modelers: Implications for automated support.