Aquatic jr v2 iss2 nov2014

Volume 2, Issue 2, 2014 November - 2014 ISSN : 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
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vironmental boundaries, including interactions
among geological, microbial, biological, chemical,
physical, hydrological, and societal processes
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and marine environments
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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
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and evaluate ecological issues related to processes
and structures at different integration levels
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ronmental risk issues
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indicators, modelling, impact of environmental
factors on population fluctuations
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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
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Volume 2, Issue 2, 2014
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Volume 2, 2014 Issue 2, 2014
Editorial 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
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ISSN : 2320-6772
Members of Editorial Board
Of
November, 2014
Editor-in-Chief
Prof. Manik Sinha
Former Dean, Faculty of Law,
Dr R.M.L Awadh University, Faizabad (UP),
Senior Advocate, Govt Of India, High Court, Lucknow
Email: manik.sinha@ymail.com
Chief Editor
Prof. S C RAI
Associate Dean-cum-Principal College of Fisheries, Dholi
(Muzaffarpur),Rajendra Agricultural University, Bihar
E-mail : drscraicofdholi@rediffmail.com
Coordinting Editor
Dr. Laxman Prasad
College of Fisheries Sciences,N D University of Agricultural
& Technology, Faizabad
E-mail: vermalp@yahoo.com
Publication Editor
Mrs. Manisha Verma, B.Sc., B.tech.
Publication Editor(Chief Executive)
Email : manisha@manishanpp.com, 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-
khandIndia
E-mail: shahineetu@rediffmail.com
Dr. A.K.Jaiswar
Senior Scientist 402 Type IV quar-
ters, Central Institute of Fisheries
Education ICAR, Panch Marg, Off
Yari Road, Mumbnai 400061
E-mail: akjaiswar@yahoo.co.in &
akjaiswar@cife.edu.in
Dr. 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: vermalp@yahoo.com
Dr. (Mrs)Haninder Maini
Deptt. of Zoology and Biotechnology
Govt. M.H. College Of H.Sc. And Sc. For
Women, Jabalpur (M.P.)
Email: hanindermaini@gmail.com
Prof. K R Singh
Retd. Prof of Zoology, B R A Bihar Uni-
versity
Email : drmamatakumari@gmail com
Dr. Bela Ztshi
Dept of Zoology
Bangalore University, Bangalore 560056
Email: Bela_zutshi@yahoo.co.in
Dr. Braj kishor Mishra
B R A Bihar University, Muzaffapur
E-mail: brajkp.mishra@gmail.com
Assistant Editors
Dr. Shivendra Kumar
Department of Fisheries, Rajendra Agricultural University Dholi,
Samastipur
Email: shiv.dholi@rediffmail.com
Dr. 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: shahineetu@rediffmail.com
Dr. A.K.Jaiswar
Senior Scientist 402 Type IV quarters, Central
Institute of Fisheries Education ICAR, Panch
Marg, Off Yari Road, Mumbnai 400061
E-mail: akjaiswar@yahoo.co.in & akjaiswar@
cife.edu.in
Dr. B. B. Nayak
PhD,Principal Scientist, Central Institute of
Fisheries Education Versova, Mumbai- 400061
E- mail: binyan@yahoo.com

Of
Volume No. 2 Issue No. 2, 2014
C o n t e n t s
S. No. Title Page No.
1.
3.
4.
01
17
32
November, 2014
20
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 Education
ImplementationAndAssessment,Analysis of Data of winter 2010 Data
Barun Maity
2.

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1 ••
Int. J. Aqu. Sci. & Tech. Vol. 1 (2) 2013, pp. ISSN: 2320-6772ISSN: 2320-6772
www.manishanpp.com
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: bela_zutshi@yahoo.co.in; nazimanr@yahoo.co.in; iamsreekala@gmail.com
(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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Int. J. Aqua. sci. & Tech. Vol. 2 (2) 2014, pp.1-16

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© 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.
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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: Sadhanakesharwani.23@gmail.com
AbstractAbstract
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: 03Pages: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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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 2
Comparison 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 EDUCATION
Barun Maity
Department of mathematics
Pingla Thana Mahavidyalaya, Maligram, Paschim Medinipur, West Bengal, 721140
Email ID: barunmaity1986@gmail.com
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 the
relationship 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 ways
not 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: 16Pages:10
AbstractAbstract

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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-
icsinthecourseusingthemidtermgrades.
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
previousstudentpreparednessmuchmore
preciselywiththegradefromthefallsemes-
terF09calculuscourse.Themodelusedby
AngusandWatsondidthisthroughtheuse
of dummy variables that indicated which
mathematics courses the students took in
highschool,butnotindividualperformance
in these courses. The model we will
use to analyze the data is as follows,
FEi
==α0
+α1
MTi
+α2
FALLi
+α3
LOWi
+α4
HIGHi
+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 con-
sidered and carried out, but these did not
result in an improved fit. Interpretability
of the parameters on the original scale of
measurementwasdesired,sothedecision
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-
tweenscoresontheMapleT.A.quizzesand
final exam grades, after accounting for the
midterm scores and final grade from the
F09 semester. We introduce
the following linear model,
FEi
== γ0
+γ1
TAi + γ2
MTi
+γ3
FALLi
+ 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’sgradefromtheF09semesterand
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
fromthepreviouscourseisnotsurprising.
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
thestudentwillperformonaclosed-book
final exam. The midterm tests are very
similar to the final exam in both the way
theyareadministeredandquestionstyle,
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 explana-
tory 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
researchquestionisthatthismodelwillad-
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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27 ••
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FEi
= β0
+ β1
TAi
+ β2
AvgAttemptsi
+ 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(denotedasAvgAttempts).
The point of interest for this model is
estimating the relationship between the
numberofattemptsperquizandfinalexam
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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28 ••
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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
differentfromzero.Thisindicatesthateach
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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29 ••
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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, only
ss said that they used the Maple version
of the course notes, modifying them as
needed, for their own investigations after
class. 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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30 ••
www.manishanpp.com
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.
REFERENCES
1. 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.,“Extendedmatchingsetsques-
tionsforonlinenumeracyassessments:
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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31 ••
www.manishanpp.com
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
ofthestatisticonlinecomputationalre-
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-
biningonlineandpaperassessmentin
aweb-basedcour’seinundergraduate
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”,EducationalStudiesinMathemat-
ics, vol 58, No 2, 2005, pp. 235-252.
1131
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.andGriffin,F.,“Online
quizzes for operation research a case
study” , Asian Social Science,
Vol 4, No 3, 2008, pp. 26-35.
1151
15. Hoskins,S.andvanHooff,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 AIDS
Gourhari Das
Pingla thana Mahavidyalaya, Maligram, Pingla, West Bengal, pin-721140
Email ID: barunmaity1986@gmail.com
ଁୗ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
INTRODUCTION
Sampling 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
AbstractAbstract
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33 ••
www.manishanpp.com
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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34 ••
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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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Prof. T.R.C Sinha a true lover of science
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Prof. Sinha and endeavour to make his dream
of promoting science and help young scientists,
publish their articles and encourage them in
their research.
Manisha Verma

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emphasis is to involve a large community of scientists specially JUNIOR SCIENTISTS and scholars from India and
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articles , Review articles Book reviews, Interviews, Short communication , Letters to the Editor ,Case reports and News
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tioned and strictly according to the guide lines.
In the 1st Part:
1) Title 2)Name of the organisation where work was carried out 3)Name of the Author with whom correspondence is to
be made along with his/her Mob.No. 4)Date of Submission 5)No. of Tables 6)No .of Photographs 7)Numbers of Graphs
8)No. of References 9)Name and addresses of at least Three referees along with their Email I/Ds and Mob Nos.
In the 2nd Part :
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4) Introduction 5) Materials and methods 6) Results 7) Discussions (if any) 8) Acknowledgements 10) References
(Under References heading, Name of the Journals should be in italic letters) and (11) Name of The journal.
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References
A. For articles in a Journal:-Walsh, J.E. (2008) Climate of the Arctic Marine Environment. Ecological Applications. 18. pp. 3-22.
B. For Books:-Ward, D.R. (2002) Water Wars: drought, floods, folly and politics of thirst: River head Books. New
York. p. 12.
C. Chapter in a book:-Andrews, T.J., Clo ugh, B.F. and Muller, G.J. (1984). Photosynthetic gas exchange properties and carbon
isotope ratios of some mangroves in North Queensland. In: H.J. Teas (Ed.), Physiology and Management of Mangroves. W. Junk.
The Hague. pp. 15-23.
From website:-National Oceans and Atmospheric Administration (NOAA). 1995. Regional Perspectives: Indian
Ocean. www.ncdc.noaa.gov /paleo.outreach /coral/sor/sor_indian.html , accessed on July 13, 2008.
While giving reference of more than two authors in the text, after, the name of the first author, et.al., should be used, followed by the
year of publication.
The literature cited should list the author’s name, year of publication, title of the paper, and the Journal titles(italic let-
ters) which should be cited in full (no abbreviation) with volume number and page numbers, as indicated below:
All remittances are to be on line credited in the account of Academic And Research Publications in the
PUNJAB NATIONAL BANK, VAISHALI BRANCH, GHAZIABAD, IFSC: PUNB0405300
Current Account No. 4053002100011106 and inform the at manisha@manishanpp.com accordingly.
Prof.Manik Sinha-THE EDITOR-IN-CHIEF, H.Office: EC 41, Maya Enclave, New Delhi -110064
Email: manik.sinha2@gmail.com, manisha_npp@yahoo.com. www.manishanpp.com.

Volume No. 2, Issue No. 2, 2014
C o n t e n t s
Reviewed Articles
1.
2.
3.
01
17
22
November, 2014
ACADEMIC AND RESEARCH
PUBLICATIONS
H.Office: EC 41, Maya Enclave,
New Delhi -110064
Email : manik.sinha2@gmail.com ,
manisha_npp@yahoo.com
www.manishanpp.comwww.manishanpp.com
www.manishanpp.com
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 Education
Barun Maity
Audio, Visual And Audio Visual Aids
Gourhari Das
4. 32

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