International Journal Of Aquaticscience And Technology : Issn 2320-6772

Volume 2, Issue 2, 2014

November - 2014 ISSN : 2320-6772

This Journal is an academic and peer-reviewed publication (Print ISSN 2320-6772 )

International Journal Of

Aquatic Science & Technology

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An International Refereed Journal

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CALL FOR PAPERSWe invite you to submit high quality papers for review and possible publication in all areas of aquatic science and technol-ogy which includes wetlands, freshwater and marine aquatic systems Ranges from molecular-level mechanistic studies to investigations at the whole-ecosystem scale, including fish ecology, Living resources as environmental and diversity indicators. All authors must agree on the content of the manuscript and its submission for publication in this Journal before it is submitted to us. Manuscripts should be submitted by e-mail to the Editor at [email protected]

To promote and encourage specially YOUNG SCIENTISTS to take active part in research and get acquainted with the latest development and research in the field of aquatic science and technology. To promote cooperation in the pursuit of research in general and to exchange and contribute to the progress in the field of aquatic science and technology in particular.

Following types of papers are invited for publication in this Journal :-a) Original Research Papers of Scientific values b) Review Papersc) Short Communications d) Case Reports e) Letters to the Editor f) As you see

TYPES OF PAPERS ARE INVITED

REVIEWERS PROCESSAll manuscripts are reviewed by an editor and members of the Editorial Board or qualified outside REVIEWERS. Deci-sions will be made as rapidly as possible and the Journal strive to return reviewer’s comments to authors within 6 weeks. The Editorial Board will re-review manuscripts that are accepted pending reviewers. It is the goal of the this Journal to publish manuscripts within 4 weeks after submission after getting O K report from the Author.

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ABOUT THE JOURNALINTERNATIONAL JOURNAL OF AQUATIC SCIENCE AND TECHNOLOGY is a biannual an academic and peer-reviewed Journal published by ACADEMIC AND RESEARCH PUBLICATIONS. It was published from year i.e. 2013. The ISSN No. of the JOURNAL is - 2320-6772 .

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1. Covers wetlands, freshwater and marine aquatic systems and their boundaries

2. Ranges from molecular-level mechanistic studies to investigations at the whole-ecosystem scale, in-cluding fish ecology

3. Reports on research across disciplinary and en-vironmental boundaries, including interactions among geological, microbial, biological, chemical, physical, hydrological, and societal processes

4. Covers the ecology of fresh, brackish, estuarine and marine environments

5. Presents timely reports on fundamental and ap-plied research in the field and the laboratory

6. Provides a forum for aquatic ecologists to address

and evaluate ecological issues related to processes and structures at different integration levels

7. Population genetics, plasticity in the life-history of aquatic animals and plants

8. Stock assessment and survey methodology, dy-namics of exploited ecosystems

9. Production techniques, nutrition, reproduction, infectious diseases relevant to aquaculture, envi-ronmental risk issues

10. Living resources as environmental and diversity indicators, modelling, impact of environmental factors on population fluctuations

11. Any other related topic.

For all publication matters related to the Journals Acceptance letter for publication of articles , Invoice, Reprints etc. should be sent directly to the PUBLICATION EDITOR whose address is as follows :

To,

Er. Manisha Verma, B.Sc., B.Tech.Publication Editor (Chief Executive Director)

Academic And Research PublicationsH.Office: EC 41, Maya Enclave, New Delhi -110064

Email : [email protected] , www.manishanpp.com

For publication of your article, Acceptance letter, Review Reports , Status Report , and all other queries related to your articles, should be sent directly to the Editor-in-Chief , whose address is as follows:Prof. Manik Sinha, THE EDITOR-IN-CHIEF Email : [email protected] , Contact at : 09415155631

Academic And Research Publications

I J A qS TInternational Journal

Of Aquatic Science & Technology

Volume 2, Issue 2, 2014November, 2014

Academic And Research PublicationsH.Of f ice : EC 41 , Maya Enclave, New Delhi -110064

Cite this volume as 2(2)IJAqST(2014) and so on. . . .

This Journal is an academic and peer-reviewed publication (Print ISSN 2320– 6772 )

© Journal on Aquatic Science and Technology. All rights reserved. No portion of material can be reproduced in part or full without the prior permission of the Editor.

Note : The views expressed herein are the opinions of contributors and do not reflect the stated policies of the

Academic And Research Publications.

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services if covered in these secondary publications”


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Volume 2, 2014 Issue 2, 2014Editorial Board

© Journal on Aquatic Science and Technology. All rights reserved. No portion of material can be reproduced in part or full without the prior permission of the Editor.Note : The views expressed herein are the opinions of contributors and do not reflect the stated policies of the ACADEMIC AND RESEARCH PUBLICATIONS. Correspondence: All enquiries, editorial, business and any other, may be addressed to: The Editor-in-chief, International Journal of Aquatic Science and Technology (IJAqST), H.Office: EC 41, Maya Enclave, New Delhi -110064Email : [email protected], [email protected] , www.manishanpp.com.

ISSN : 2320-6772

Members of Editorial Board


Aquatic Science & TechnologyNovember, 2014

Editor-in-ChiefProf. Manik Sinha

Former Dean, Faculty of Law,Dr R.M.L Awadh University, Faizabad (UP),

Senior Advocate, Govt Of India, High Court, LucknowEmail: [email protected]

Chief Editor Prof. S C RAI

Associate Dean-cum-Principal College of Fisheries, Dholi (Muzaffarpur),Rajendra Agricultural University, Bihar

E-mail : [email protected]

Coordinting EditorDr. Laxman Prasad

College of Fisheries Sciences,N D University of Agricultural & Technology, Faizabad

E-mail: [email protected]

Publication EditorMrs. Manisha Verma, B.Sc., B.tech.Publication Editor(Chief Executive)

Academic And Research PublicationsH.Office: EC 41, Maya Enclave, New Delhi -110064

Email : [email protected], www.manishanpp.com

Dr. Neetu Shahi Scientist

Biotechnology-Animal Science-Directorate of Coldwater Fisheries Research(Indian Council of Agri-

cultural Research) Bhimtal 263 136 Nainital, Uttara-

khandIndiaE-mail: [email protected]

Dr. A.K.JaiswarSenior Scientist 402 Type IV quar-ters, Central Institute of Fisheries Education ICAR, Panch Marg, Off

Yari Road, Mumbnai 400061E-mail: [email protected] &

[email protected]. B. B. Nayak

PhD,Principal Scientist, Central Institute of Fisheries Education

Versova, Mumbai- 400061

Prof. Safal Kumar Mishra B R A Bihar ,University, Muzaffapur

E-mail: [email protected]. (Mrs)Haninder Maini

Deptt. of Zoology and BiotechnologyGovt. M.H. College Of H.Sc. And Sc. For

Women, Jabalpur (M.P.)Email: [email protected]

Prof. K R SinghRetd. Prof of Zoology, B R A Bihar Uni-

versityEmail : drmamatakumari@gmail com

Dr. Bela Ztshi Dept of Zoology

Bangalore University, Bangalore 560056Email: [email protected]

Dr. Braj kishor Mishra B R A Bihar University, Muzaffapur E-mail: [email protected]

Assistant EditorsDr. Shivendra Kumar

Department of Fisheries, Rajendra Agricultural University Dholi, Samastipur

Email: [email protected]. Himanshu Priyadarshi

College of Fisheries, Tirupura

Dr. Neetu Shahi Scientist

Biotechnology-Animal ScienceDirectorate of Coldwater Fisheries Research(Indian Council

of Agricultural Research) Bhimtal 263 136 Nainital, UttarakhandIndia

E-mail: [email protected]. A.K.Jaiswar

Senior Scientist 402 Type IV quarters, Central Institute of Fisheries Education ICAR, Panch

Marg, Off Yari Road, Mumbnai 400061E-mail: [email protected] & akjaiswar@

cife.edu.in

Dr. B. B. NayakPhD,Principal Scientist, Central Institute of

Fisheries Education Versova, Mumbai- 400061E- mail: [email protected]


Aquatic Science & TechnologyVolume No. 2 Issue No. 2, 2014

C o n t e n t s

S. No. Title Page No.

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November, 2014

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Qualitative And Quantitative Analysis of Phytoplankton In A Lentic Water Body Of Jabalpur (M.P.)

Sadhana Kesharwani

Variation In Acid And Alkaline Phosphatase Activity In Tissues of Freshwater Fish, L.rohita With Response To Pollutants In Freshwater Lakes Of Bangalore, Karnataka

Nazima Noor, Sreekala G And Bela Zutshi

Audio, Visual And Audio Visual Aids

Gourhari Das

Technology In Mathematics EducationImplementation And Assessment, Analysis of Data of winter 2010 Data

Barun Maity

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Int. J. Aqua. sci. & Tech. Vol. 2 (2) 2014, pp.

VARIATION IN ACID AND ALKALINE PHOSPHATASE ACTIVITY IN TISSUES OF FRESHWATER FISH,

L.ROHITA WITH RESPONSE TO POLLUTANTS IN FRESHWATER LAKES OF BANGALORE, KARNATAKA

Nazima Noor, Sreekala G And *Bela Zutshi

* Professor, Department of Zoology,Bangalore University, Jnanabharathi, Bangalore-56, India

Email: [email protected]; [email protected]; [email protected]

(Date of Receipt : 23-10-2014; Date of Acceptance for Publication : 02-11-2014 )

With recent development in indus-tries and sudden population growth,

Ànalysis conducted on the water quality of the two lakes A (Vengaiah lake - receiving sewage from adjacent storm water drain) and lake B (Yellamallappa Chetty lake - re-ceiving industrial effluents from an adjacent pharma company) revealed significant variation in the physico-chemical parameters of lake B. The results were compared to Hebbal fish farm (control site). The aim of the present study was to evaluate the efficiency of acid and alkaline phosphatase activity as bio-indicators, in muscle, gill, kidney, liver and brain tissue of fresh water fish, Labeo rohita reared in these water bodies for commercial purposes. The activity of acid phosphatase (ACP) and alkaline phosphatase (ALP) in gill, muscle, kidney, liver and brain tissues of a fresh water fish, Labeo rohita sampled from these water bodies was assessed. A marked percentage decrease in ACP & ALP activities was observed in all the fish tissues from lake B due to the presence of pollutants such as trace metals. The fish tissues revealed the follow-ing decreasing trend in ACP percentage: brain > kidney > liver > gill > muscle whereas trend in ALP was: brain = liver > kidney > gill > muscle. A significant decline in the level of these phosphatases was observed indicating that fish was under stress due to its continuous exposure to metals and other chemicals in polluted water of lake B. This inturn triggered changes in biochemical constituents, metabolic pathways, dysfunc-tioning and cellular damage in various tissues. ACP and ALP were positively correlated with each other in general.

INTRODUCTION

Pages : 16 References : 49

Key Words : Acid Phosphatase, Alkaline Phosphatase, Freshwater Lake, Physic-Chemical Parameters.

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treated and untreated effluents and domestic sewage are constantly be-ing discharged into fresh water bod-

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ies (lakes) which change the proper-ties of water and adversely affect the flora and fauna of that particular wa-ter ecosystem. This series of changes in the physicochemical characteris-tics of water, have been the subject of several investigations (Mahanana-da et. al., 2010). High level of varia-tion in physical and chemical param-eters was noted in Chandola lake of Ahmadabad by Pradeep et. al., (2012). The water quality parameters like temperature, hardness, pH, dissolved gases (oxygen and CO2), salinity etc. must be watched regularly, individual-ly or synergistically to keep the aquat-ic habitat favourable for existence of fish (Mondal et. al., 2010). Among en-vironmental pollutants, metals are of particular concern, due to their poten-tial toxic effect and ability to bioaccu-mulation in aquatic ecosystems (Mill-er et. al., 2002). The presence of toxic metals in environmental matrices is one of the major concerns of pollution control and environmental agencies in most parts of the world (Tay et. al., 2009) and are widely distributed in the environment with sources mainly from the weathering of minerals and soils (Merian, 1991). Fish is an extreme-ly reliable component of an aquatic monitoring system because they in-tegrate the effect of detrimental envi-ronmental change as consumers and occupies a relatively high position in the aquatic food chain. Enzyme de-terminants of fish can be applied as an indicator of chemical intoxication (Cengiz and Vnlu, 2006). Enzyme sys-tems also play a significant role in food utilization and metabolic activity (Bols et. al., 2001). But since this sys-tem may get altered under the stress

of pollutants it can be considered as one of the major biomarker indicat-ing the level of changes, consequent of pollutants in the tissues, organs and body fluid of the fish that can be recognized and associated with es-tablished health impairment process (Akinrotimi et. al., 2009). Evaluation of such enzyme activities in the tissue and organs of aquatic organs in the diagnosis of the effects of pollutants is one of the emerging areas in toxi-cological monitoring and remedia-tion programmes (Oluah et. al., 2005). These enzymes have also been used as bioindicators of heavy metals in-toxication because of their sensitivity to metal pollution (Mora et. al., 2004).

Phosphatase is a hydrolytic enzyme, leading to the release of ortho-phos-phate from phosphorus compound and based on the optimum pH of action environment, classified into acid phosphatase (ACP, EC 3.1.3.2, optimum pH≤6.0) and alkaline phos-phatase (ALP, EC 3.1.3.1, optimum pH≥8.0) (Jansson et al., 1988). The phosphatases (ACP and ALP) are im-portant biomarkers because they are involved in adaptive cellular response to the potential cytotoxicity and gen-otoxicity of pollutants (Velisek et. al., 2006). Both ennzymes are metalloen-zyme, involved in various metabol-ic processes, such as permeability, growth and cell differentiation, pro-tein synthesis, absorption and trans-port of nutrients, and gonadal matu-ration (Ram and Sathayanesan, 1985). Enzyme analysis of organs such as muscles, kidney, liver, heart and gills in fish can provide important informa-

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tion about the internal environment of the organism (Boeger et. al., 2003). In fisheries sciences, changes in phos-phatase activities have been regard-ed as indices of growth, illness and spawning of fish (Matusiewicz and Da-browski, 1996).Work on phosphatases has been carried out on different fish-es by various scientists like Kågedal et. al., (2001) and Sreenivasan et. al., (2011); etc to know the physiologi-cal conditions of the fish. The present study was aimed to assess the physi-co-chemical parameters of lakes A, B and control site and to evaluate the efficiency of acid and alkaline phos-phatase activity as bio-indicators, in muscle, gill, kidney, liver and brain tis-sue of fresh water fish, Labeo rohita reared in these water bodies for com-mercial purposes. Correlation within ACP & ALP activity of tissues of fish and also with water parameters of lake A & B was monitored for a period of one year and compared with fishes from control site.

MATERIALS AND METHODS

Based on the lake survey, two lakes Ven-gaiah lake (lake A) and Yellamallappa Chetty lake (lake B) were selected for the present study. Hebbal fish farm was opt-ed as Control for both water quality and fish tissue analysis. It was observed that lake A received domestic sewage and lake B received agricultural run-off and industrial effluents from nearby pharma industry. Water samples were collected in water sampling bottles from each lake in the morning at about 07.00 to 07.30

am at an interval of once every fortnight for a period of one year for its qualitative analysis. The various physico-chemical parameters like temperature, pH, BOD, COD, DO, TDS, conductivity, acidity, alka-linity, phosphates, sulphates, and nitrates were determined by following standard methods by (APHA et. al., 2005).

Test fish, Labeo rohita were sampled at the same time as water sampling time period from (Control), lake A and lake B. They were then anaesthetized us-ing MS222, dissected and the tissues such as muscle, gill, kidney, liver and brain were carefully excised and trans-ferred to a suitable medium for record-ing the enzymatic activities. Activities of ACP and ALP in these tissues were determined by using Spectrophotomet-ric Stop Rate Determination method (Bergmeyer et. al., 1974). Each assay was replicated six times and the values are expressed as mean ± SD. Data was then analysed statistically by ANOVA followed by Tukey’s test.

RESULT AND DISCUSSION

Physico chemical parameters of water samples from control, lake A and lake B were statistically analyzed and the data represented in table 1 showed signifi-cant mean differences at p<0.001 and 0.01 among control and lake A and those of lake B. All water parameters of con-trol site were compared with lake A and lake B and inturn with the standard BIS: 10500-1991(Revised 2012).The data re-vealed high level of pollution in lake B when compared to lake A, control site and BIS standard due to the presence of an industry on its bank, agricultural run-off, idol immersion during festival season

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and discharge of domestic sewage and solid waste through various sources. The quality of water depends on the concentration of its physico-chemical parameters but high levels of tempera-ture, total suspended solids, chemical oxygen demand, biological oxygen de-mand, conductivity, turbidity and alka-linity was noted in lake B. Trace metals’ content such as, aluminium, cadmium, copper, iron, lead and mercury in the present study also showed relatively high concentration in water samples of lake B when compared to BIS values and lake A. High conductivity and low dissolved oxygen usually associated with heavy metals and industrial efflu-ents will alter the activity of hydrolytic enzymes like esterases and phosphates of the fish exposed to them (Ambrose et al., 1994). Heavy metal contamination may have devastating effects on the ecological balance of the recipient en-vironment and on the diversity of aquat-ic organisms (Farombi et. al., 2007). Toxicants also can inhibit the activ-ity or synthesis of enzymes resulting in decreased activities in the organs. The negative effects of these pollutants are detrimental to the aquatic inhabitants, including fishes (Olaifa et. al., 2004).

Gabriel and Akinrotimi (2011) noted that enzymes can be used to confirm and asses fish exposure to toxicants, provid-ing a link between external and internal structure and degree of responses to toxicant exposure observed between different individuals. The metabolic pathways of fish are affected by vari-ous pollutants, organic and inorganic chemicals due to the alteration of cellu-lar enzymatic activities. Variation in the

metabolic enzyme activities in fish is di-rectly proportional to the concentration of the toxicant (Pesce et. al., 2009).

Phosphatases are mainly localized at cell membrane. Any damage in the cells may result in alteration in phosphatases activity (Shabnam and Badre Alam, 2012). The changes in the enzyme ac-tivities disrupt physiological and bio-chemical processes (De la Torre et. al., 2000). In the present study, the tissues selected were – Muscle , important tis-sue to conduct movement; Gill, vital res-piration organ, which was targeted by lots of xenobiotics due to their extensive surface area directly contacted with water environment and the reduced distance between the internal and ex-ternal medium (Jiang et. al., 2012); Kid-ney, the excretory and immune organ of fish; Liver , one of the vital detoxifying organs and important compartment of heavy metal accumulation (Fallah et. al., 2011); and brain, a major component of the central nervous system and the main target of the pollutants (Mieiro et. al., 2011). The activity of ACP and ALP in these tissues of fish showed a positive correlation with each other regardless of the three water bodies. .A shift was seen in the activity of phosphatases from lake B when compared to control and lake A making them a stress marker as also stated by (Gabriel et. al., 2012). A marked decrease in the activity of phos-phatases in these tissues was recorded from lake B when compared to lake A and control ones. The percent decrease in the reduction of the phosphatases activity in the fish tissues from control to two lakes are represented in fig. 1 to 10. A maximum decrease in activity of ACP

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was shown by brain tissue (26%) when compared to other tissues {kidney (20%), liver (15%), gill (11%) & muscle (10%)} in lake B due to the significant correla-tion with levels of COD and sulphate. Gill tissue showed negative correlation with nearly all water parameters excepting those of BOD and COD level (Table 2). ALP activity significantly decreased in muscle (12%), gill (15%), kidney (19%), liver (23%) and brain (23%) and showed negative correlation with various water parameters (Table 2). Change in the physico-chemical parameters of lake B affected the cell and cellular constitu-ents and hence enzymes of the fish un-der study. Acid phosphatase (ACP) and Alkaline phosphatase (ALP), both differ in their sub cellular distribution. ACP is as-sociated with lysosomes where as ALP activity was found to be highly concen-trated in plasma membrane enriched fraction. Any alteration in phosphatase activity is a direct manifestation of dam-age or dysfunction in experimental or-gans of fish (Ogueji and Auta, 2007).

ACP could sequester trace metals in lys-osomes of eukaryotic cells which inturn could alter the structure, permeability and integrity of lysosomal membranes resulting in enzyme diffusion into cyto-sol (Hedayati et. al., 2010). Trace metals along with other chemicals present in the lake caused inhibition in the activ-ity of various enzymes (ACP & ALP) due to distortion in the cell organelles, in-creased permeability of plasma mem-brane or cell necrosis thus disturbing the physiological state of the fish (Akanji et. al., 2008) heavy. Decrease in the activity of ACP in brain, kidney, liver, gill and mus-cle tissue of fish in the present investiga-

tion indicated disturbance in structure of cell organelles and lysosomal disruption which would release hydrolytic enzyme into cytoplasm leading to auto degrada-tion of cellular proteins and cell damage (Palanisamy et. al., 2012). Mathur and Gupta (2008) reported that toxic chemi-cals affect the permeability of cell mem-branes, disturbing energy metabolism and cell functions by releasing hydrolas-es due to increased fragility of lysosomal membranes.

Alkaline phosphatase is a intrinsic plas-ma membrane bound enzyme found in almost all animal cells. According to Mo-lina et. al., (2005) any disturbance in the membrane property caused by interac-tion with xenobiotics could lead to altera-tion in ALP activity. A hindrance in trans-portation of molecules and substances across the cell membranes of the fish tis-sues sampled from lake B might be due to the pollutants causing disruption in membrane transport system resulting in significant decrease in ALP activity. This decline in ALP activity can be attributed to leakage of the enzyme from cytosol across the damaged plasma membrane into extracellular fluid and general blood circulation or decreased enzyme syn-thesis on account of organ dysfunction (Sunmonu et. al., 2009) or due to reduc-tion in concentration or total absence of specific phospholipids required by this membrane- bound enzyme to express its full activity (Yakubu et. al., 2002).

According to Ramalingam and Vimalade-vi (2002) reduction in ALP activity affect-ed the metabolic processes such as the synthesis of nuclear proteins, nucleic ac-ids, phospholipids, etc while augmenta-

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tion of enzyme may be a signal of tissue damage (Atli and Canli, 2007). Similar re-sults of ACP and ALP activity was report-ed by Humtsoe et. al., (2007) in liver and muscle of Labeo rohita exposed to arse-nic. Kamble et. al., (2011) have studied the decreased activities of ACP in liver, muscle and kidney of the fish Barilius burna exposed to dimecron. Das and Mukherjee (2003) reported depletion of alkaline phosphatase due to sub-lethal exposure of Labeo rohita fingerlings to Cypermethrin resulting in biosynthesis shift and energy metabolism pathway of the exposed organism (Ovuru and Mgbere, 2000).

The order of decrease in the level of phosphatases from brain to muscle tissue showed maximum reduction in brain as it gets affected due to continu-ous exposure of fish to the pollutants such as trace metals in lake B over a period of time. This in turn caused an increased inhibition of the enzyme ac-tivity as also reported by Ansari and An-sari, (2012) in the fish exposed to pes-ticide Alphamethrin; neurodegenerative damage due to passage of pollutants through the fish blood-brain barrier into the brain tissue reported by Berntssen et. al., (2003). Significant reduction of ACP and ALP activities in kidney which is an excretory and immune organ of fish, sampled from lake B suggested sensitivity of both enzymes related to bioaccumulation of trace metals (Pala-

niappan and Karthikeyan, 2009). Marr et. al., (1995) pointed out that a metal- binding protein, the metallothionein (MT), could be induced by heavy met-als in liver, and there existed a positive correlation between MT and heavy metals. MT in liver can attenuate cy-totoxicity induced by heavy metals by sequestering these metals and reduc-ing their intracellular concentration. In the present study, decreased ACP and ALP activities in liver of fish sug-gested high toxicant concentration in liver was beyond the regulation ca-pacity of MT which inturn caused a re-duction in the detoxification capacity of liver eventually leading to the liver damage. Gills are direct target of pol-lutants and toxicants in the external medium and hence more sensitive to their bio-accumulation leading to re-duction in the activities of ACP and ALP (Jiang et. al., 2012). In muscle tis-sue ACP and ALP activities showed a decline which might be associated with less bioaccumulation as sug-gested by Jiang et. al., (2012) as con-tractile proteins a major component of muscles have a high affinity for cal-cium and low affinity for heavy metals (Palaniappan and Karthikeyan, 2009). The results indicate that pollutants along with trace metals disrupted the biochemical processes and physiological equilibrium of the fish, causing stress that would de-crease its nutritional value.

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Comparative study of heavy metal and trace element accumulation in edible tissues of farmed and wild rain-bow trout (Oncorhynchus mykiss) Us-ing ICP-OES Technique. Microchem J. 98:275–279.

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17. Gabriel, U. U., Akinrotimi, O. A. and V. S. Ariweriokuma, 2012. Changes in met-abolic enzymes activities in selected organs and tissue of Clarias Gariepi-nus exposed to Cypermethrin . Journal of environmental engineering and technol-ogy. 1(2): 13-19.

18. Hedayati, A., Safahieh, A., Savar, A. and J. G. Marammazi, 2010. Assessment of aminotransferase enzymes in yellow-fin sea bream (Acanthopagrus latus) under experimental condition as bio-markers of mercury pollution. World J Fish Mar Sci. 2: 186-192.

19. Humtsoe, N., Davoodi, R., Kulkarni, B. G. and B. Chavan, 2007. Effect of arse-nic on the enzymes of the rohu carp, Labeo rohita (Hamilton, 1822). Raff. Bull. Zool. 14: 17-19.

20. Jansson, M., Olsson, H. and K. Petters-son, 1988. Phosphatases: Origin, char-

acteristics and function in lakes. Hyd-robiologia. 170:157–175.

21. Jiang, H., Yang, H., Kong, X., Wang, S., Liu, D. and S. Shi, 2012. response of acid and alkaline phosphatase activi-ties to copper exposure and recovery in freshwater fish Carassius auratus gi-belio var. Life Science Journal. 9(3): 233-245.

22. Kågedal, K., Zhao, M., Svensson, I. and U. T. Brunk, 2001. Sphingosine-induced apoptosis is dependent on lysosomal proteases. Biochem J. 359: 335 –343.

23. Kamble, S. M, Bhagwan, H. K. and V. M. Pulate, 2011. Effect of Sublethal con-centration of Dimecron on acid phos-phatase and alkaline phosphatase ac-tivities in liver, muscle and kidney of a fresh water fish Barilius burna. Int Ref Res J. 2 (19): 46-47.

24. Mahananda, M. R., Mohanty, B. P. and N. R. Behera, 2010. Physico-Chemical Analysis of Surface and Ground Water of Bargarh District, Orissa, India. IJR-RAS. 2 (3): 284-295.

25. Marr, J. C. A., Bergman, H. L., Lipton, J. and C. Hogstrand, 1995.Differences in relative sensitivity of naive and metals acclimated brown and rainbow trout exposed to metals representative of the Clark Fork River, Montana. Can J Fish Aquat Sci. 52:2016-2030.

26. Mathur, S. and A. K. Gupta, 2008. His-toenzymological study on the toxicity of Copper Sulphate in the digestive glands of Lymnaea luteola. Journal of Environmental Biology. 29(2): 201-204.

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27. Matusiewicz, M. and K. Dabrowski, 1996. Utilization of the bone/liver alka-line phosphatase activity ratio in blood plasma as an indicator of ascorbate deficiency in salmonid fish. Proc Soc Exp Biol Med. 212:44–51.

28. Merian, E. 1991. Metals and their compounds in the environment. Oc-currence, analysis and biological rel-evance. UCH, Wienheim-New York- Basel-Cambridge.

29. Mieiro, C. L., Pereira, M. E., Duarte, A. C. and M. Pacheco, 2011. Brain as a critical target of mercury in environ-mentally exposed fish (Dicentrarchus Labrax)–Bioaccumulation and oxida-tive stress profiles. Aquat Toxicol. 103: 233–240.

30. Miller, G. G., Sweet, L. I., Adams, J. V., Omann, G. M., Passino-Reader, D. and P. G. Meter, 2002. In vitro toxic-ity and interactions of environmental contaminants (Arochlor 125 and mer-cury) and immunomodulatory agents (lipopolysaccharidae and cortisol) on thymocytes from lake trout (Salvelinus namaycus). Fish Shellfish Immun. 13: 11- 26.

31. Molina, R., Moreno, I., Pichardo, S., Jos, A., Moyano, R., Monterde, J.G., A. Cameán, 2005. Acid and alkaline phos-phatase activities and pathological changes induced in Tilapia fish (Oreo-chromis sp.) exposed subchronically to microcystins from toxic cyanobac-terial blooms under laboratory condi-tions. Toxicon.46:725–735.

32. Mondal, D. K., Kaviraj, A. and S. Saha, 2010. Water quality parameters and

fish biodiversity indices as measures of ecological degradation: A case study in two floodplain lakes of India. J. Water Resource and Protection. 2:85-92.

33. Mora, S. D., Sheikholeslami, M. R., Wyse, E., Azemard, S. and R. Cassi, 2004. An assessment of metal con-tamination in coastal sediments of the Caspian Sea. Mar Pollut Bull.48:61-77.

34. Ogueji, E. O. and J. Auta, 2007. The Effects of sub-lethal doses of Lamb-da-Cyhalothrin on some biochemical characteristics of the African catfish, Clarias gariepinus. J. Biol. Sci. 7(8): 1473-1477.

35. Olaifa, F. G., Olaifa, A. K. and T. E. On-wude, 2004. Lethal and sublethal ef-fects of copper to the African cat fish (Clarias gariepnus). Afr. J. Biomed. Res. 27: 9 – 15.

36. Oluah, N. S., Ezigbo, J. C. and N. C. Anya, 2005. Effect of exposure to sub-lethal concentrations of Gammalin 20 and Actellic 25ec on the liver and se-rum lactate dehydrogenase activity in the fish Clarias albopunctatus. Animal Research International. 2(1): 231 – 234.

37. Ovuru, S. S. and O. Mgbere, 2000. Enzyme changes Shrimps (Penaeus notialis) following a brief exposure to weathered bonny light crude oil. Delta Agric. 7: 62–68.

38. Palaniappan, P. R. and S. Karthikeyan, 2009. Bioaccumulation and depuration of chromium in the selected organs and whole body tissues of freshwater fish Cirrhinus mrigala individually and in binary solutions with nickel. J Environ

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Sci. 21: 229–236.

39. Palanisamy, P., Sasikala, G., Mallikaraj, D., Bhuvaneswari, N. and G. M. Na-tarajan, 2012. Activity levels of phos-phatases of the airbreathing Catfish Mystus cavasius exposed to electro-plating industrial effluent chromium. Biol. Med. 4(2): 60-64.

40. Pesce, S. F., Cazenave, J., Monferra, M. N., Frede, S. and D. A. Wunderlin, 2009. integrated survey on toxic effects of Indane on neotropical fish; Corydoras-paleatus and Jenynsiamultidentata. Environ. Poll. 156:775-783.

41. Pradeep, V., Deepika, C., Gupta, U. and S. Hitesh, 2012. Water quality analy-sis of an organically polluted lake by investigating different physical and chemical parameters. International Journal of Research in Chemistry and Envi-ronment.2 (1): 105-111.

42. Ram, R. and A. G. Sathayanesan, 1985. Mercuric chloride, cythion and ammo-nium sulfate induced Changes in the brain, liver and ovarian alkaline phos-phatase content in the fish Channa puntactus. Environ Ecol. 3:263–268.

43. Ramalingam, V. and V. Vimaladevi, 2002. Effect of Mercuric Chloride on membrane-bound enzymes in rat tes-tis. Asian J Androl. 4:309–311.

44. Shabnam, A. and A. Badre Alam, 2012. Alphamethrin toxicity: Effect on the reproductive ability and the activities

of phosphatases in the tissues of ze-brafish, Danio rerio. International Journal of Life Sciences and Pharma Research. 2(1): 89 – 100.

45. Sreenivasan, R. S., Krishna Moorthy, P. and M. Deecaraman, 2011. Effect of Phosphatases activity in the hepato-pancreas and muscle of the fresh wa-ter female field Crab, spiralothelphusa hydrodroma (Herbst) treated with cy-permethrin. Int.J.of Pharma.Sci. And Drug Res. 3(2): 123-126.

46. Sunmonu, T. O., Owolabi, O. D. and O. B. Oloyede, 2009. Anthracene induced enzymatic changes as stress indica-tors in African catfish, Heterobran-chus bidorsalis (Geoffroy Saint Hilaire, 1809). Res J Environ. Sci. 3(60): 677-686.

47. Tay, C. K., Asmah, R. and C. A. Biney, 2009. Trace Metal Levels in Water and Sediment from the Sakumo II and Muni Lagoons, Ghana. West African J. Appl. Ecol. 16: 75-94.

48. Velisek, J., Wlasow, T., Gomulka, P., Svobodova, Z., Dobsikova, R., Novo-tny, L. and M. Dudzik, 2006. Effects of Cypermethrin on rainbow trout (On-corhynchus mykiss). Vet. Med-Czech. 51(10): 469-476.

49. Yakubu, M. T., Olatunji, I. K. and M. A. Akanji, 2002. Comparative effects of administration of Halofantrine hydro-chloride and Dihydroartemisinin on some enzymes of selected rat tissues. NISEB J. 2: 175-180.


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QUALITATIVE AND QUANTITATIVE ANALYSIS OF PHYTOPLANKTON IN A LENTIC WATER BODY

OF JABALPUR (M.P.)

Dr. Sadhana Kesharwani

Asstt. Prof. Department of Zoology Govt. M.H.College of H. Sc. & Sc. For Women, Jabalpur (M.P.)

Email: [email protected]

Abstract

Plankton forms the basic link of food chain for all aquatic animals and thus, play a key role in fishery. The pollution of surface water by discharge from human activities is one of the major environmental problems faced worldwide. Effluent discharge in large pro-portions resulting in Eutrophication. This leads to excessive growth of algae. Pollution may selectively stimulate the growth of a few types of phytoplankton and that in turn reduces the water quality. The selective type of algae that exist in polluted water are being used as indicators of Pollution (Yazdandoost and Katadare, 2001). The change in physicochemical condition of water can be reflected in biotic community of water. Therefore physicochemical characteristics of pond water was also studied to calcu-late the water quality of experimental pond.Key Words : Phytoplankton, Pollution, Eutrophication.

INTRODUCTION

References: 03 Pages:05

Phytoplankton are the grass of lakes and oceans and most of the species have worldwide distribution. They are free floating algae ranging from single celled Picoplankton to Colonial forms. The liv-ing organism are now well known as the causative agents for polluting the environ-ment. Symptoms of eutrophication that seem to concern people most are un-wanted phytoplankton blooms.

In the present communication, the diver-sity of phytoplankton has been described

for a period of two years in Amkhera pond of Jabalpur with special reference to pol-lution. Although physicochemical analysis of water was also performed but not de-scribed here is details.

MATERIAL AND METHODS

Amkhera pond is located at a distance of about 4 km from the Jabalpur city beside Jabalpur Puraina road. The catchment area of pond is 24 hectares and depth is more than 10 meters. The pond receives domestic drainage from all sides of sur-rounding basti. The samples were col-

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(Date of Receipt : 16-10-2014; Date of Acceptance for Publication : 10-11-2014 )

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lected (from 5 stations of the pond) regu-larly at an interval of 30 days for a period of two years. Collection of samples were performed by filtering 25 litres of water through phytoplanktonic net and were preserved in 5% formaldehyde (APHA. 1985). The size of net is number 25, mesh diameter of the pore is 60 m (Trivedy and Goel, 1986). Collected samples were con-centrated upto 5 ml. Physicochemical analysis performed according to APHA (1985).

The samples were observed under 10 X and 45X magnifications. They were sketched with all details under high power (45X). The figures were identified through standard Monographs and keys (Adoni; 1985, Trivedy and Goel; 1986).

RESULTS AND DISCUSSION

During March 2000 to Feb. 2002 twenty seven phytoplanktonic spe-cies were recorded from the surface and bottom water of Amkhera pond. The species observed, belonged to three major groups viz. Cyanophy-ceae, Chlorophyceae and Bacillari-ophyceae. Out of 27 species, 5 spe-cies belonged to Cyanophyceae group. 11 species belonged to Chlo-rophyceae group, 11 species be-longed to Bacillariophyceae group.High density of phytoplankton was observed during monsoon and winter season. While low density was observed during summer sea-son. Each group showed different peaks at different times, In general maximum number of species were observed on surface whereas two species (Lyngbya and Nostoc) were

totally or paritly absent at the bot-tom at few stations. Physico-chem-ical variations also play a vital role in their distributional pattern both in quality and quantitiy. High phyto-planktonic fauna in pond can be at-tributed to some extent to the hard and alkaline water (Kesharwani, 2004) and this is in accordance with the observations of Jana (1978), Islam (1990) and Shastree et. al., (1993). During ecological stud-ies of pond Amkhera, it was noticed that the biological equilibrium is dis-turbed by the domestic sewage.

Among Cyanophyta, the most domi-nant genera of this group was Ana-cystis and was present throughout the study period. Oscillatoria se-cured second position in order of dominance as far as density is con-cerned at surface but at the bottom it was found totally absent during 2000-01 and partly present during the next year. Chlorophyceae was the second main dominant group of Phytoplankton. The more com-mon genera of this group were Scendesmus. Microspora, Golen-kinia, Chlorella, Closterium Cru-cigenia, Pediastrum and Tetrapora. Among Bacillariophyceae; genera like Fragilaria, Nitzchia, Navicula, Synedra, Gomphonema and Rho-palodia were more common.

Qualitative observations of phyto-planktonic population clearly indi-cates the eutrophic status of wa-terbody by presence of Anacystis, Fragilaria, Chlorella, Closterium, Pediastrum, Scendesmus, Navicu-

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la, Navicula, Nitzchia and Synedra (Palmer, 1969; Trivedy and Goel, 1986; Khare, 1993; Yazdandoost and Katadare, 2001;. Chouhan et. al., 2013. By the presence of these al-gae we can conclude the pollution-al status of a waterbody in question. Thus, algae can be used as bioindi-cators of water pollution. Table 2 provide comparison of wa-ter quality at surface and bottom of Amkhera pond. Water quality of sur-face water for the year 2000 – 2001 – 2002 (78.80) was lesser than the

WQI recorded for bottom (80.40) It means that the surface water is more polluted than the bottom. As far as number of species is con-cerned each group showed dif-ferent peaks at different times. Results of biological monitoring are supported by physiochemical analysis of water. Based on these nine parameters water quality In-dex was calculated as proposed by Horton (1965). Results of both studies showed the polluted sta-tus of Amkhera pond.(Kesharwani, 2004).

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TABLE 1 : State of occurrence of Phtoplankton at Surface and bottom of

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1. Adoni, A.D. (1975). Studies on micro-biology of Sagar lake. Ph.D. Thesis, Univ. of Sagar, 254 pp.

2. Adoni, A.D. (1985). Work book on Lim-nology, Indian Map Committee, Deptt.

Of Environment. Govt. of India.

3. APHA (1985). Standard methods for the examination of water and waste water (16th ed.) APHA, AWWA and WPCF. Washington. D.C..

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REFERENCES

Table 2Comparison of annual average range of physcio-chemical parameters of five sta-tions of Amkhera pond, Jabalpur their quality rating (in bracket) and calculated W.Q.I. during 2000-2001-2002.

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TECHNOLOGY IN MATHEMATICS EDUCATIONImplementation And Assessment, Analysis of Data of winter 2010 Data

Barun Maity

Department of mathematics

Pingla Thana Mahavidyalaya, Maligram, Paschim Medinipur, West Bengal, 721140

Email ID: [email protected]

In the Fall 2009 (F09) and Winter 2010 (W10) semesters, the first year calcu-lus course n question were not taught by the creator of the ma0ple content used in the class. The instructor of these courses did implement all the technol-ogy listed at the beginning of this chap-ter. The instructor implemented one major change in W10: instead of having ten weekly.

online quizzes consisting of 8-12 ques-tions each, there were 18 quizzes con-sisting of 2-4 questions each. The ques-tions used on the quizzes were the same as in previous years.

We wish to investigate the effects of technology on student learning.This is difficult to assess, as there are many confounding factors involved. These factors include a student’s previous

Ťhe use of technology has become highly popular in mathematics education. Instruc-tors have implemented technology into classroom lessons, as well as various appli-cations outside of class room. The data analysis of the online quizzes focuses on two years of five year study.Linear regression techniques are employed to investigate therelationship between find exam grades and both how a student interacts with and per-forms on the outline quizzes. The testing system was utilized to provide students with regular formative quizzes. Based on techniques learned through the literature search, the data analysis focuses on how students interact with the testing system students interact with the testing system and not necessarily just their performance on the on-line quizzes. The availability of information such as the number of times a student at-tempted each quiz and the date on which the student first attempted each quiz allows our data analysis to test the effect of how the student interacts with the system in waysnot found in the literature. Student attitude towards the online testing is assessed through survey results.

INTRODUCTION

Key Words: Technology into Classroom Lesson, Data Analysis, Online Outline Quizzes.

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References: 16 Pages:10

Abstract(Date of Receipt : 14-09-2014; Date of Acceptance for Publication : 17-10-2014 )

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mathematical preparation, the student’s willingness to utilize the technology the way the instructor intends and overall student effort to the subject material.

We will include several explanatory var-iables in our models to try to account for some of these factors.

We focus on the winter semesters for two reasons, the first being that stu-dents should be well acquainted with

the technology used and more impor-tantly, we can use the student’s grade from the fall semester F09 as a measure of student preparedness. One measure of student learning over the semester is their score on the final exam. We will use this score as our response variable.The explanatory variables we will inves-tigate are the student’s overall grades on the maple T.A. quizzes, the com-bined grade of all three midterms, the student’s________________

Model 1: Analysis of num-ber of quizzes completed

The first model we will use was adapted from Angus and Watson[1]. We look to explain student learning through only the usage of the formative online quizzes, not the student’s performance on these quiz-zes. The model accounts for the student’s previous mathematical prepar-edness using the grade from the previous semester, and the student’s in-course mastery of the top-ics in the course using the midterm grades. We measure each stu-dent’s usage of the online quiz-zes by the number of the 18 quizzes the student completed. The main advan-

tage of this model over the model used by Angus and Watson is that we can metsure previous student preparedness much more precisely with the grade from the fall semes-ter F09 calculus course. The model used by Angus and Watson did this through the use of dummy variables that indicated which mathematics courses the students took in high school, but not individual performance in these courses. The model we will use to analyze the data is as follows,

FE i==α0 +α1MTi +α2FALLi + α3LOWi + α4HIGHi +εi ....(3.1)

where the response variable is the final exam grade, denoted as FE. The midterm

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grade is denoted MT, the grade from the fall course is labeled FALL. The variables of interest are LOW and HIGH. The vari-able LOW is assigned a value of 1 if the student completed 9 or less of the 18 on-line quizzes during the semester, 0 other-wise.HIGH is assigned a value of 1 if the students completed 16 or more of the online quizzes, 0 otherwise. These variables group students into

three groups: low use, moderate use and high use of the online quizzes. There were 29 students with low usage. 52 with usage and 349 with high usage. Figure 3.3 gives some indication that students who wrote a higher number of quizzes tended to perform better on the final exam. The goal of this model is to assess whether simply using the online quiz system is as-sociated with a higher final exam grade.

This model was run using ordi-nary least squares regression tech-niques. Issue with heteroscedas-ticity were observed (see Figure 3.4), as the variance appears to be smaller for larger fitted val-ues. Common transformations, such as the log it, were considered and carried out, but these did not result in an improved fit. Interpretability of the parameters on the original scale of measurement was desired, so the decision

was made to run models on the untrans-formed data. In the inference procedures, heteroscedasticity was accounted for by using robust standard errors based on the heteroscedastic consistent covariance matrix (HCCM) (White [27]). Based on the recommendation of Long[17], the HC3 variation of the HCCM was chosen. Com- putations were carried out in the statisti-cal package R, using the package car. The results given by this mod-el can be found in Table 3.2.

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We note from Table 3.2 that most of our explanatory variables are behaving as ex-pected. A higher grade on the midterms or in the previous course is associated with a higher exam score. There is no significant relationship between students with high and moderate usage or between low and moderate usage after accounting for the fall grade and midterm scores. The com-parison between low and high usage also yielded no significance after accounting for fall and midterm grades (estimated coefficient for HIGH 0.50,p-value 0.79).

MODEL 2: ANALYSIS OF PERFOR-MANCE ON MAPLE T.A. QUIZZES

Here the model of interest involves stu-dent performance on the online quizzes. We wish to see if there is a relationship be-tween scores on the Maple T.A. quizzes and final exam grades, after accounting for the midterm scores and final grade from the F09 semester. We introduce the following linear model, FEi == γ0+γ1TAi + γ2MTi+γ3FALLi + εi ....(3.2)

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We continue to use final exam score our response variable. We account for student mathematical preparedness using the student’s grade from the F09 semester and midterm grades as we did in Model 1.

We see that the estimated coefficient for T.A. score is positive, but not significantly different from zero. The fact that these scores are not sig-nificant after accounting for the written midterm grade and the grade from the previous course is not surprising. The students have unlimit-ed attempts and access to their class notes while completing the Maple T.A. quizzes, mak-ing these grades not an ac-curate representation of how the student will perform on a closed-book final exam. The midterm tests are very similar to the final exam in both the way they are administered and question style, which is one why the midterm grades are the best predictor of final exam grades.

If we run the linear model with just the T.A. and fall semester grades as explanatory variables, we do find a sig-nificant positive relationship be-tween Maple T.A. and final exam

The variable of interest is each student’s combined grade on all Maple T.A. quizzes from the semester (denoted as TA). The results provided by this mod-el are available in Table 3.3.

grades (estimated coefficient 0.17. p-value < 0.001). There is significant evidence of a relationship be-tween T.A. grades and fi-nal exam grades, after adjusting for the fall semester grades. If midterm grades are also included in the model, the T.A. effect is no longer significant.

MODEL 3: ANALYSIS OF NUM-BER OF ATTEMPTS PER QUIZ

Students were allowed an unlimited num-ber of attempts at each online homework quiz; the class averaged 4.17 attempts on each quiz (see Figure 3.5). Our main research question is that this model will ad-dress is, “What is the relationship between taking extra attempts to achieve the same grade on the online tests and final exam performance?”. The plot of av-erage attempts versus fi-nal exam score (see Figure 3.6) seems to indicate a negative re-lationship. To address this ques-tion, we introduce a linear model,

Table 3.3: Results from Model 2 for the W10 semester (Equation (3.2)).

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FEi = β0 + β1TAi + β2AvgAttemptsi + εi. (3.3)

The response variable is the fi-nal exam score. Our explana-tory variables are TA(each student’s grade on the Maple T.A. quizzes) and the average number of attempts per quiz completed (denoted as AvgAttempts). The point of interest for this model is

estimating the relationship between the number of attempts per quiz and final exam grade, after accounting for the stu-dent’s score on the online quizzes.

The results provided by this model can be found in Table 3.4. We see the coefficient for our variable TA is positive and signifi-cantly different from zero, indicating that a

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high grade on the online quizzes is related to a higher grade on the final. We also see our variable of interest, AvgAttempts, has a negative coefficient that is significantly different from zero. This indicates that each extra attempt per quiz students took to achieve the same score on the online quizzeswas associ-ated with a 4% decrease in final exam score. The 95% confidence interval for the size of this effect is -5.10% to -3.03%, We offer two possible explanations for the sign and magnitude of this coefficient. The first explanation is that the stronger stu-dents in the class will achieve a high grade on the TA. quizzes in a lower number of attempts, and the second is that students following the “T.A. Protocol” outlined at the beginning of this chapter may both lower their number of attempts required and improve thier understanding. Based on the success of students who are known to be following the protocol, it is the belief of the author that following this pro-tocol would result in mastery of the course material. These speculations cannot be tested formally as this would require each student’s work habits to be monitored in order to determine which strategies they are implementing.

RESULTS OF THE ATTITUDINAL SURVEY

Student were asked to compete a survey near the end of the Winter 2010 semes-ter to measure student attitude towards the technology in the classroom. There were 324 respondent out of 483 regis-tered students. A full version of this survey is available in Appendix B of this thesis. The responses to the Maple T.A. quizzes were very positive. Perhaps the most tell-ing statistic form the survey is that 90 % of students agreed with the statement “The T.A. quizzes helped me learn” (50 % strongly agree, 40 % agree, 7 % no opin-ion, 3 % disagree, 0 %strongly disagree). This shows that the students see the val-ue in the earning tool, which is a big step in getting students to use it properly. As for the rest of the technology used in the classroom,83% of students believed that “Overall, I benefited fro the inclusion of technology in the course” (34% strongly agree, 49% agree, 11% no isagree, 1% strongly disagree).The survey also shows the importance of making these quizzes count towards the student’s final grade, since 56% of the students admitted that if the quizzes did not count for grades, they

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would not make time to writ them. This shows that even through the quizzes are designed to be formative, it will be difficult to get students to complete them if they are not treated as a summative assess-ment.

The survey also highlighted the difficulty of getting students to use new technology on their own. Even though 62% of the re-spondents agreed that the in-class Maple demonstrations helped them learn, onlyss said that they used the Maple version of the course notes, modifying them as needed, for their own investigations afterclass. This is a frustrating aspect of using technology in the classroom; the students know the power of the software and are provided with the opportunity to use it on their own, but are resistant to adopting it for their own independent use.

CONCLUSION AND DISCUSSION

All of the above linear models were run again using a logistic transformation, and this did not improve the fit or change the conclusions in any meaningful way. We also ran the models investigating the number of attempts and starting dates of quizzes including the grade for the fall course as a proxy for student mathemati-cal preparedness, which did not change the effects in any significant way. Noting a possible introduction of bias using the average attempts and average starting date only on quizzes the student actually completed, we also ran these models for the W11 semester using only the 414 stu-dents who competed all 10 quizzes; this did not change the results in any notable fashion.

We also investigated but did not report on the relationship between the T.A. quizzes and the midterm grades. We ran simple models using each midterm grade as the response variable with the explanatory variables being the student’s grade in the fall course and the student’s grade on the T.A. quizzes taken before that midterm test. These models all showed a significant and positive relationship between the on-line quiz performance and midterm score (estimated coefficients on T.A. scores be-tween 0.1 and 0.2, p-value<0.001).

A final point of discussion is the treatment of missing data.

Students who dropped the course during the semester and did not write he final were omitted; to our knowledge, no stu-dent dropped the course because of the technology used in the course. Students were allowed to count only two of the three midterms and have a more heavily weighted final. The midterm grade for any student who chose to only write two mid-terms was taken to be the average grade on those two tests, ignoring the missed test. Two students in W10 and one student in W11 did not complete any online quiz-zes, these students were omitted from the models including the average attempts per quiz. Finally, only students who took math*1200 in the fall semester previous to the semester in question were includ-ed in any models using the fall grade as an explanatory variable; as mentioned before in both semester discussed, ap-proximately 88% for the class completed Math*1200 in the previous fall semester.

We have seen evidence of a significant and positive relationship between the use

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of online Maple T.A. quizzes and student performance. We observed even after ac-counting for in course mastery of the ma-terial and previous mathematical prepar-edness that students who complete more of the online quizzes tend to perform bet-ter on the final exam. We have also seen a positive impact of taking a fewer number of attempts to achieve the same score on the online quizzes. We found that al-though performance on T.A, is related to performance on the final exam, it is not as strong of an indicator as midterm grads or from the previous course. Finally we founded students who attempted quizzes before the due date performed better on the exam.

We have also seen the student attitude towards the technology is positive, with most students believing that the quizzes help them learn. We see that although the quizzes are intended to be a forma-tive assessment, it is important that there is a small grade attached to each quiz to a motivate students to complete them.

The take-home message of this analysis is that students who use the online quiz-zes the way they are intended (i.e., start quizzes early, reduce the number of at-tempts required per quiz, complete each weekly quiz) tend to perform better on the final exam.

REFERENCES1. Angus, S. and Watson, J., “Does reg-

ular online testing enhanceng in the numerical sciences? Robust evidence from alarge data set”, British Journal of Educational Technology, Vol 40, No2, pp. 255-272.

2. Ashton, H., Beevers, C., Korabinski, A.

and Youngson, M.,Incorporating par-tial credit in computer-aided assess-ment of mathematics in secondary education”, British Journal of Educational Technology,, Vol 37, NO 1, 2006, pp. 93-119.

3. Bonham, S., Deardorff, D. and Beich-ner, R., “Comparison of student perfor-mance using web and paper-based homework in college-level physics”, Journal of Research in Science Teaching, Vol 30, No 10, 2003, pp. 1050-1071.

4. Brown, B. and Liedholm, C., “Can web course replace the classroom in prin-ciples of microeconomics? : The Ameri-can Economic Review, Vol 92, No 2, 2002, pp. 444-448.

5. Bruce, C. And Ross, J., “Trigonometry and linear growing patterns CLIPS fiel.tests 2009-2010: a mixed methods study”, Report Submitted to: Ministry of Education, Ontario, August 2010.

6. Cann, A., “Extended matching sets ques-tions for online numeracy assessments: a case study” , Assessment Evalu-ation in Higher Education, Vol 30, No 6, 2005, pp.633-640.

7. Cassady, J., Budenz-Anders, J. , Pav-lechko, G. and Mock, W.. “The effects of internet-based formative and summative assessment on test anxiety, perceptions Of threat, and achievement”, paper presented at the Annual Meeting Of the American Educational Re-search Association, 2001. Re-treived May 20, 2011 from http : / /vvv. eric. ed. gov/PDFS/ED453815. pdf. 181 .

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8. Cazes, C., Gueudet, G., Hersant, M. and Vanderbrouck, F. , “Using E-exercises bases in mathematics: studies at uni-versity” , Intemational Journal of Com- puters for Mathematical Learn-ing, VOI I l, 2006, pp. 327-350.

9. Dinov, 1., Sachchez, J. and Christou, N., “Pedagogi-cal utilization and assessment of the statistic online computational re-source in introductory probability and statistics courses” , Elesevier Computers e Education, Vol 50, 2008, pp. 284-300. 1101

10. Fox, J. and Weisberg, S. An R Companion to Applied Re-gression, Second Editaom Thousand Oaks CA: Sage, 2011. http://socserv.socsci.ncmaster.ca/jfox/ Books /Conpani on.

11. Engelbrecht, J. and Harding, A., “Com-bining online and paper assessment in a web-based cour’se in undergraduate mathematics”, Journal Of Computers in Mathematics and Science Teaching, Vol 23, NO 3, 2004, pp. 217-231. 1121

12. Engelbrecht, J. and Hard-ing, A. , ‘Teaching undergrad-uate mathematics on the internet. Part 1: technologies and tax-onomy” , Educational Studies in Mathemat- ics, vol 58, No 2, 2005, pp. 235-252.

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13. Griffin, F. and Gudlaugsdottir. S., “Using online randomised quizzes to boost student performance in math-ematics and operations re-search” , Paper presented at International Confer-ence on Information Tech-noloo Based Higher Education and Training (7th : 2006). Retriesed May 20, 2011 from http://ieeexplore. ieee.org/stanp/stanp.jsp?arnunber.04141663. 1141 .

14. Gudlaugsdottir, S. and Griffin, F. , “Online quizzes for operation research a case study” , Asian Social Science, Vol 4, No 3, 2008, pp. 26-35. 1151

15. Hoskins, S. and van Hooff, J., “Motivation and ability: which students use online learning and what influ-ence does it have on their achievement?” , British Journal of Educational Technology, vol 36, No. 1-2, 2005 pp. 177-192. 1161

16. Jacobs, K.L, “Investigation of interactive online visual tools for the learning of mathematics” , International Journal of Mathematical Education in Science and Technology, vol 36, No 7, 2005, pp. 761-768.

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AUDIO, VISUAL AND AUDIO VISUAL AIDSGourhari Das

Pingla thana Mahavidyalaya, Maligram, Pingla, West Bengal, pin-721140

Email ID: [email protected]

ଁୗhe present investigation falls exclusively under the Survey Studies. As survey studies, here also the study is conducted to collect details description of existing phenomena with the intent of employing data to justify current conditions and practices and to make more intelligent plans for improving them. Survey Studies may be broad or narrow in scope. Here the area of investigation is limited to the selected some schools of Guwahati city.

This investigation also takes the form of a School Survey as it serves the purpose of the investigative, evaluative and status survey which serves primarily to evalu-ate existing conditions of audio, visual and audio visual aids in some schools under the study. School surveys may be conducted at the local, regional, state or national level. They may undertake at various levels at instruction- elementary, secondary, secondary or higher. School survey may be concerned with the prob-lems of school building, available financial resources, pupil transportation system, acquiring information about the characteristics of educational personal, informa-tion about the behavior patterns of pupils in classrooms, pupil’s knowledge, and skills, intelligences etc, information about equipment and supplies, such as the number and kind the library books or the amount and types of laboratory, ath-letic and audio, visual and audio visual equipment. Some school surveys may be concerned with the curriculum, text books and other instructional materials. The present investigation is also conducted at the locality level i.e. the Guwahati city and as a school survey it concerned with providing valuable information about the Various aspects of teachers’.

Key Words: Audio, Visual And Audio Visual Aids In Some Schools Under The Study. School Surveys .

Pages:3 References: 04

INTRODUCTIONSampling is a process by which a rela-tively small number of individuals, ob-

jects, events or institutions is selected and analyzed in order to find out some-thing about the entire populations from

Abstract(Date of Receipt : 03-09-2014; Date of Acceptance for Publication : 02-10-2014 )

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1. Kuma (1998) took up a study. He re-ported that teacher by and large had positive attitude towards the utiliza-tion of educational media, but lacked training in educational media. A few teachers felt that they had poor me-dia operating capability. So media utilization is found to be poor.

2. The document brought by NCTE on “Competency based and Commit-ment oriented teacher Education” ((1998),January) highlighted that im-portance of teacher’s competencies in the field of preparation and utiliza-tion of teaching learning materials.

3. “Teachers’ Professional Develop-ment for the Technology enhanced Classroom in the schools of tomor-row”

4. The above article written by Hen-ric Hanson (E-Learning, Volume 3, November4, (2006)). This article is concerned with two important is-sues i. e., the role of the teacher in the technology enhanced classroom and how to prepare the teachers for using new technology in the class-room.

5. Aruichelvan and Viswanathan (2008) pointed out that the effectiveness, utilization and scope of wri (Radio, television and Internet) in the field of teaching learning. In India there is still much more scope for further de-velopment, expansion and more

which it was selected.

So sample is a smaller representative of a large whole. In other words, a sec-tion of the population selected from the latter in such a way that they are rep-resentative of the universe is called a sample. Thus the representative pro-portion of the population is called a sample. Usually four steps are involved in the process.

1. Defining the population.2. Listing the population.3. Selecting a representative sample.4. Obtaining an adequate sample.

In the present study, the investigator has adopted the incidental or acciden-tal type of non probability sampling. The units of the population are selected at the investigator’s own discretion. The investigator has collected information from all the school that are conveni-ently available. The idea behind select-ing such sample technique was that the investigator wanted to get the desired information of the population within a limited period of twelve to fourteen months. By adopting such a sampling technique, the researcher has collected information about the schools.

The schedule has many advantages. It is administrated personally and there-fore it provides opportunity to the re-searcher to establish rapport with the respondents. It helps the researcher to explain the nature and purpose of in-vestigation and to make the meaning of the questions clear to the respondents if they misinterpret a question or give incomplete or indefinite responses. The

schedule also economizes time and ex-pense of the investigation. It ensures al-most all complete and usable returns.

CONCLUSION

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1. effective utilization of RTI.2. Singh, (1990) A survey in four states

( Jammu and Kashmir, Karela, Oris-sa and Uttar Pradesh ) revealed that teachers in karela use maximum audiovisual aids. Among the aids, audio cassett is in highest demand. The same state does spend highest amount in audiovisual aids in com-parision to rest of the states covered in the study.

3. Solachi ( 1991 ) revealed that radio, newspaper, journals and magazines, audio tape recorder, microphone, books, chalkboard, wall maps, globes and still pictures are avail-able in all the schools. 40% schools have TV sets, 60% schools have pup-pets and toys.

4. R. R. Naik ( 2003 ) revealed that uni-versity libraries in Karnataka have a meager collection of multime-dia resources in comparision with the print media resources and they

REFERENCE

1. Altelcar, Dr. A.S.(1992) Education in Ancient India. Vanarasi: Manohar Prakashan.

2. Aggarwal, J.C.(1994) Landmarks in the History of Modern Indin

3. Joyce,Bruce Weil,Marsha(2003)Models of Teaching New Delhi: Prentice-Hall of India PVT. Ltd.

4. Thangasamy, Dr. Kokilas (2006) Instructional Technology and Cur-riculum Development. New Delhi: Neelkamal Publications PVT. Ltd..

have not shown a good collection of audiovisual resources.show that the experimental students obtained significantly higher scores on the performance of reading and writ-ing communication than the control group of students.

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Qualitative And Quantitative Analysis of Phytoplankton In A Lentic Water Body of Jabalpur (M.P.)

Sadhana Kesharwani

Variation In Acid And Alkaline Phosphatase Activity In Tissues of Freshwater Fish, L.rohita With Response To Pollutants In Freshwater Lakes Of Bangalore, Karnataka

Nazima Noor, Sreekala G And Bela Zutshi

Technology In Mathematics EducationImplementation And Assessment, Analysis of Data of winter 2010 Data

Barun Maity

Audio, Visual And Audio Visual Aids

Gourhari Das

4. 32

International Journal Of Aquaticscience And Technology : Issn 2320-6772

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