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Background: Honey has multiple therapeutic properties due to its composition with diverse components. Objectives: This study aims to investigate the antimicrobial efficacy of Saharan honeys against bacterial pathogens, the variation of honey floral origins, and its physicochemical characteristics. Experience the power of PDF through a full functioned PDF Reader. Quickly learn the product by utilizing the Microsoft Office style ribbon toolbar, which provides a familiar user interface. Leverage existing forms and workflow with standard PDF. Qcm biologie animale pdf Qcm biologie animale pdf Examen de Biologie et. De Biologie Vegetale.

Abstract

Background: Honey has multiple therapeutic properties due to its composition with diverse components.

Objectives: This study aims to investigate the antimicrobial efficacy of Saharan honeys against bacterial pathogens, the variation of honey floral origins, and its physicochemical characteristics.

Materials and Methods: The antimicrobial activity of 32 samples of honey collected from the Algerian Sahara Desert was tested on four bacteria; Bacillus subtilis, Clostridium perfringens, Escherichia coli, and Staphylococcus aureus. The botanical origin of honeys and their physicochemical properties were determined and their combined antibacterial effects were modeled using a generalized linear mixed model (GLMM).

Results: Out of the 32 study samples, 14 were monofloral and 18 were multifloral. The pollen density was on average 7.86 × 10⁶ grains/10 g of honey, water content was 14.6%, electrical conductivity (EC) was 0.5 μS/cm, pH was 4.38 ± 0 50, hydroxymethylfurfural (HMF) content was 82 mg/kg of honey, total sugars = 83%, reducing sugars = 71%, and the concentration of proline = 525.5 ± 550.2 mg/kg of honey. GLMM revealed that the antibacterial effect of honey varied significantly between bacteria and floral origins. This effect increased with increasing of water content and reducing sugars in honey, but it significantly decreased with increase of honey EC. E. coli was the most sensitive species with an inhibition zone of 10.1 ± 4.7 mm, while C. perfringens was the less sensitive. Honeys dominated by pollen of Fabaceae sp. were most effective with an overall antimicrobial activity equals to 13.5 ± 4.7 mm.

Conclusion: Saharan honeys, of certain botanical origins, have physicochemical and pollinic characteristics with relevant potential for antibacterial purposes. This encourages a more comprehensive characterization of honeys with in vivo and in vitro investigations.

Introduction

In recent years, pathogenic microorganisms have developed multiple drug resistance due to the abundant and wide spared use of antimicrobial drugs that were commonly used in human medicine (Al-Waili et al., ; Noori et al., ). Even with the broad spectrum of some antibacterial agents, the choice of most suitable remains relatively limited due to the development of bacterial resistance, breakthrough infections, and ever-increasing therapeutic problem (Shahid et al., ). Alternative antimicrobial strategies are therefore urgently needed using various natural, traditional, and nonconventional sources (Al-Waili et al., ; Lucera et al., ). Antimicrobial substances originated from natural resources have been widely exploited for this purpose, with a specific focus of studies on a specific product “Honey” due to a long tradition of use within various medical and food systems (Lusby et al., ; Al-Waili et al., ). Honey is used to treat certain topical infections and even for accelerating wound healing and epithelization (Simon et al., ; Mandal and Mandal, ).

Honey is used for centuries and still widely used as an antiseptic where its main characterized role is the prevention and limitation of bacterial infection derived largely from biochemical properties related to peroxide generation via glucose oxidase activity (Brudzynski, ), nonperoxide effect such as, osmolarity, acidity, aromatic acids, phenolic, and other phytochemical compounds such as methylglyoxal (Mundo et al., ; Lusby et al., ; Lee et al., ; Mavric et al., ). Moreover, honey serves as a natural antioxidant and a rich source of minerals, carbohydrates, proteins, and vitamins with nutraceutical and probiotic properties (Bertoncelj et al., 2007; Begum et al., 2015).

In addition, the antibiotic and antiseptic effects of honey have been scientifically proven in several studies (Shamala et al., 2002; Werner and Laccourreye, 2011). These effects are mainly due to the bio-chemical composition of honey that contains high sugar and low water concentrations with low pH. These properties generate the high osmolarity that produces the antimicrobial action (Wahdan, ). Honey also contains molecules inhibiting bacterial growth, such as hydrogen peroxide produced by glucose oxidase; and also the non-peroxide inhibins also known as phytochemicals composed (Cushnie and Lamb, ; Adeleke et al., ; Bell, ; Montenegro and Mejías, ).

It is noteworthy to mention that different analysis techniques of honey components may be implemented. The analysis of some of these substances requires special and sophisticated methods such as those performed using spectrophotometric assays, particularly gas chromatography-mass spectrometer (GC-MS), liquid chromatography-mass spectrometer (LC-MS), and nuclear magnetic resonance (NMR). These techniques are used to assess contents of molecules and elucidate the structure of active molecules (Bertoncelj et al., 2007; Tiwari et al., 2014, ).

The antimicrobial activities of honey have been extensively investigated against a large category of bacterial and fungal pathogens including Staphylococcus aureus, S. pyogenes, S. mutans, Bacillus cereus, Listeria monocytogens, Escherichia coli, Klesiella pneumonia, Pseudomonas aeruginosa, and Candida albicans (Mundo et al., ; Basualdo et al., ; Lee et al., ; Sherlock et al., ; Estevinho et al., ). The differences reported in antimicrobial effects of honey are dependent on its geographical origin thus the botanical source as well as time and processing harvesting, storage conditions, and the nature of pathogens tested (Sherlock et al., ; Al-Waili et al., ).

Since the antibacterial activity of honey varies depending on the floral origin (Salomon, ; Alzahrani et al., ), many studies investigated the biochemical composition and pollen contents of honeys of different melliferous plant species to determine levels of natural antibiotic compounds (e.g., Lins et al., 2003; Muñoz et al., 2007). The content of these inhibins in honey pollens depends on the plant species from which they originate in other words according to the floral origin. However, few studies examined the physicochemical composition and antibiotic properties of honey whose floral origin is derived from plant species living under extreme environmental conditions such as the Sahara Desert.

Although the honeybee (Apis mellifera) is known as a polylectic species (Reybroeck et al., 2014), honey harvested from the Saharan regions show great variability in pollen composition and density (Noori et al., ). This is mainly due to local ecological and floristic characteristics where the hive was installed. Moreover, under the environmental conditions of desert, diversity and abundance of plants are low (Bradai et al., 2015); so that the bee produces two honey categories (i) monofloral honey (i.e., mainly dominated by the pollen of one plant) in regions very little diversified in melliferous plants, or (ii) a multifloral honey (i.e., containing mixed pollen origin) when melliferous plants are diversified and abundant (Von der Ohe et al., 2004). This difference in the pollen composition in honey may result from the flowering period of plants (Campos et al., 2008). Additionally to this, the elective factor that the bee can exercise among the available melliferous plants should be considered (Reybroeck et al., 2014).

Given these facts, the aim of this study is based on the following question: do the botanical origin and pollen composition of honey affect its antibiotic properties and therefore its antibacterial potency? Moreover, the study seeks to determine if differences in physicochemical composition of honey can induce a different impact on its antimicrobial activity against pathogenic bacteria, with taking into account the botanical origin differences. Hence the interest of a physicochemical and pollen analysis of honeys of the Sahara which proves not only important to characterize these honeys and determine their floral origin, but also to determine the most effective floral origin and the parameters that have more antimicrobial effect against the bacteria tested.

Materials and methods

Choice and collection of honey samples

Honey samples of the current study were collected from nine localities in southwestern Algeria, in the region of Naama and Bechar located in the Algerian Sahara (Figure ​(Figure1),1), where the prevailing climate is hot arid. People of the Sahara, as well as the Algerian populations in general, frequently use honey as a cure for several diseases due to its multiple healing properties (Boukraâ, 2013), specifically the higher efficacy of the Saharan honey compared to that of North Africa (Boukraa and Niar, ). Undoubtedly, the spread of the use of honey in traditional and modern medicine has origins linked to the religious beliefs of Muslim people, where many Koranic and Islamic texts reveal that honey is a proven remedy.

Geographic location of the study area including honey harvesting sites (solid circles) in the region of Bechar (1, Benzireg; 2, Beni Ounif; 3, Djedid; 4, Sfissifa) and Naama (5, Ain Safra; 6, El Hamar; 7, Djneine; 8, Tiout; 9, Moghrar) located in the Desert Sahara of Algeria.

A total of 32 honey samples, 20 from Bechar and 12 from Naama, were recovered from local beekeepers just after honey extraction. Each sample was preserved under low temperature before processing to various analyzes in the laboratory.

Pollinic analysis

The pollen analysis of honeys consisted of two steps following the method of Crompton and Wojtas (1993). The first is the identification of pollen grains observed, whereas the second step was devoted to their count. All honey samples were analyzed without coloring. This allows showing the pollen grain in its natural color with its true appearance for facilitate the identification. Observations were carried out under an optical microscope at a magnification × 100.

Pollen was identified based on comparisons of the observed grains with those known in references. The latters are microscopic preparations of reference that we set up ourselves from fresh anther of local plants and with the help an Atlas of Microphotography (Reille, 1995).

For each sample of honey, the number of pollen grains in 10 g of honey was first counted and the results of that count were then classified in ascending order from I to V (see details in Yang et al., ). In parallel, this counts allowed us to make a classification of botanical taxa identified into frequencies; which determines if the honey in question comes from either (i) multiple plants pollinated by bees, so without a clear predominance of a particular plant (multifloral honey), or (ii) otherwise, honey is classified as monofloral (syn. unifloral) in which pollen grains of one plant species dominate (Von der Ohe et al., 2004).

Physicochemical analysis of honeys

For curative purposes and to benefit from this natural remedy, it is recommended to use fresh and natural honey (Bogdanov and Blumer, 2001). Since antiseptic and antibiotic substances tend to disappear—or at least to be less active—in old honeys (Lobreau-Callen et al., 2000), it is therefore required to proceed prior to physicochemical analyzes of quality control to be able to bind honey features with its microbiological activity. This concerns particularly water and sugar contents, pH, hydroxymethylfurfural (HMF), and obviously proline (Helrich, 1990; Bogdanov et al., 2002). Thus, each honey sample had undergone some physicochemical analyzes:

• Water content (WC): expressed in %, is determined using a refractometer for measuring the refractive index at 20°C with reference to the table Chataway according to the method followed by Bogdanov et al. (2002).

• pH: was determined by a pH meter on a solution composed of 10 g of honey and 75 mL of distilled water (Bogdanov et al., 2002).

• Electrical conductivity (EC): measured (in μS/cm) using a conductimeter device at 20°C of the test solution that consisted of 20% honey weighed as dry matter dissolved in distilled water and brought to a volume of 1/5 (Bogdanov et al., 2002).

• Hydroxymethylfurfural (HMF): was measured using Winkler's method (Bogdanov et al., 1999). HMF content is expressed in mg per 1 kg of honey. The HMF is a product of the degradation of fructose and glucose by intramolecular dehydration (Nombré et al., 2010). This parameter is used to control the freshness and quality of honey; thereby a value greater than 60 mg/kg indicates an old honey or the latter has undergone heat treatment degrading its properties (Oddo et al., 1999). Determining the HMF content is based on the measurement of absorbance by spectrophotometry at a wavelength of 550 nm in the presence of barbituric acid and para-toluidine.

• Total sugars: sugars represent the largest part of the dry matter of the bee's honey (Apis mellifera). Their analysis comes forth by refractometer, which is a quick and simple method (Helrich, 1990).

• Reducing sugars: The amount of total reducing sugars, expressed in% from total sugars, was determined titrimetrically according to the volumetric method (Helrich, 1990).

• Proline: The proline content (mg/kg honey) was determined using the colorimetric assay with ninhydrin following the method of Ough (1969) defined by Bogdanov et al. (2002). The proline content provides useful information on the maturity of honey and therefore can be used to detect forgeries. It is considered that honey is mature when its proline content is greater than 183 mg/kg. Lower values indicate a lack of maturity or honey falsification (Meda et al., 2005).

Antibacterial disc diffusion assays

The antibacterial activities of honeys were tested using the agar disc diffusion against four pathogens and resistant bacterial strains, namely: E. coli (ATCC25922), S. aureus (ATCC25923), Clostridium perfringens, and Bacillus subtilis. Pure strains of C. perfringens were provided by the microbiological laboratory of the hospital Mustapha Chaabani (Golea, Ghardaia, Algeria). While B. subtilis has been isolated from human feces and identified at the Microbiology Laboratory of Bachir Ben Nacer Hospital in El Oued (Algeria), using conventional phenotypic identification protocols.

C. perfringens was cultivated using anaerobic jars (GasPak system), whereas other bacteria were grown and purified on nutrient agar (NA). Bacterial inoculum suspensions containing 10⁶–10⁸ CFU/mL were prepared in sterile saline (0.9 g/L) and spread on Mueller-Hinton (MH) agar plates for each strain. Using sterile forceps, Whatman's filter discs (Ø = 5 mm), impregnated with different honeys were placed on the inoculated plates and left at 4°C for 2 h to allow the diffusion before being incubated at 37°C for 24 h. The clear inhibition zones around the discs indicated the presence of antibacterial activity of honey (Harley et al., 2010) which was measured as zone diameter in mm excluding the diameter of disc. Experiments were carried out in triplicates.

To control the susceptibility profile of the Gram-negative bacterium E. coli and the Gram-positive bacterium S. aureus, standard antibiotic discs were tested using the agar diffusion technique (EUCAST, 2013). The following antimicrobials amoxicillin/clavulanic acid (20/10 μg), gentamicin (15 μg) and trimethoprim-sulfamethoxazole (co-trimoxazole) (1.25/23.75 μg) were applied for both bacteria, whereas amikacin (30 μg), colistin (50 μg), and imipenem (10 μg) were tested against E. coli, and ampicillin (10 μg), cefotaxime (30 μg), clindamycin (2 IU), erythromycin (15 IU), fusidic acid (10 μg), spiramycin (100 μg), streptomycin (10 IU) against S. aureus.

Standard antibiotic discs of trimethoprim-sulfamethoxazole “SXT” (1.25/23.75 μg per disc) served as a positive control. This combination antimicrobial agent was tested on the Gram-negative bacterium E. coli and the Gram-positive bacterium S. aureus. The sterile H₂O served as a negative control in order to determine the minimum inhibitory concentration (MIC) of the study honeys. For that, each honey sample was used to prepare solutions of different proportion (w/v): 5, 10, 15, 20, 25, and 75%. However, no antibacterial activity was observed for all these honey dilutions. Consequently, we only analyzed data related to honey at natural state i.e., used without dilution. Despite that, we focused on the objective of characterizing Saharan honeys in relation with their diverse floral origins.

Modeling the synergistic antibacterial effects of honey features

As most of studies attributed the antibacterial effects of honey particularly to high sugar content, low water content, low pH and high concentration of flavonoids (Wahdan, ). However, these parameters furthermore vary following the botanical origins of honey and the ecological factors that influence both melliferous plants likewise the behavior, physiology and fitness of bees (Reybroeck et al., 2014). Therefore several (biotic and abiotic) parameters are involved in the variation of the honey quality and thus its antimicrobial activities, which makes taking into account all these variables to modeling its antibiotic and antiseptic activities a real challenge to achieve, specifically when it is tested against several pathogens that may react differently.

Accordingly, we used as much as relevant variables of the study honeys in a single statistical model to explain how the antimicrobial activity (diameter of inhibition zone) varies following: floral origins (pollens parameters), physicochemical characteristics of honey, and tested bacteria. We included all the study bacteria in a single model as honey is usually applied to heal diseases caused by the mixture of pathogenic bacteria. Linear mixed-effects models represent the best fit to this kind of data (Pinheiro et al., 2015). In addition, we added to the model other parameters that ensures that the honey is natural with high quality such HMF and proline which provides information about the maturity of honey (Bogdanov et al., 2002). Finally, because samples of honey were collected from different sites (several samples from the same site) in the Sahara Desert, we used generalized linear mixed model (GLMM) to deal with pseudo-replications.

Statistical analyses and modeling procedures

The values of the physicochemical parameters of the studied honeys were summarized for each botanical origin as means ± standard deviations (SD) and the range (min and max) of observations. The variation of each parameter between botanical origins was tested by the analysis of variance (One-way ANOVA) using the software R (R Core Team, 2015). Multiple comparisons of means (Tukey HSD tests) were performed afterward each ANOVA to distinguish homogeneous groups among botanical origins.

Descriptive statistics (mean, SD, and quartiles) of the antimicrobial activity were computed for each floral origin and bacterial strain based on replicates of honey samples containing that floral origin. Computations were performed using the function “numSummary” in R (R Core Team, 2015) and plotted using the package “ggplot2” (Chang, 2013).

The variation in antibacterial activity was modeled using a mixed-effects modeling procedure in R. The library “nlme” was used to test the effects of bacterial strains, floral origins as well as physicochemical parameters of honey on the dependent variable “inhibition zone.” The categorical factors (bacterial strains and floral origins) and all continuous explanatory variables (physicochemical parameters) were included in a GLMM as a fixed effect, while “honey samples” from which replications were carried out were considered as a random effect (Pinheiro et al., 2015). The interaction of the two factors of “Bacterial strains × Floral origin” was also encompassed into the model using the function “lme” and the maximum likelihood (ML) method. The effect of each factor as well as their interaction was achieved using the function “anova” with the selection of likelihood ratio (LR) test with “marginal” type because our data were unbalanced with regards to the number of honey samples of each floral origin. The Akaike information criterion (AIC) was used to select the model with the best fit. Finally, the function “Effect” was applied for constructing “effect plots” of every single explanatory variable included in the GLMM.

Results

Physicochemical and pollen parameters of honey

The physicochemical analysis of the study honeys, generally, indicated a water content of 14.6%, with an acidic pH (4.38 ± 0.50), EC = 0.5 μs/cm, a HMF content = 82 mg/kg honey. The total sugars were 83% while the reducing sugars = 71%. On average, pollen density was 7.86 × 10⁶ grains/10 g of honey, while the concentration of proline = 525.5 ± 550.2 mg/kg honey (Table ​(Table11).

Table 1

Physicochemical parameters of honey samples harvested in the Algerian Sahara desert following their floral origins.

Values of each parameter are given in means ± SD [range in square brackets], with the same superscript letters indicating no differences between means according to Tukey's post hoc tests, which followed One-way ANOVAs (F, F-value with df numerator and df denominator, P, P-value).

At the scale of pollinic composition, honeys dominated by Fabaceae sp. pollen contained less water (WC = 12.2%), while those dominated by Astragalus gyzensis were the most moisturized with WC = 15.8 ± 0.5%. The unifloral honey of Prunus persica had the highest EC value (0.57 μS/cm) and pH (5.05). The pH values of other types of honey ranged between 4.3 and 4.5. The unifloral honey of Fabaceae sp. showed the highest values of total sugars (86%) and HMF (184 mg/kg honey), but was the poorest in reducing sugars (63.4%). For the latter parameter, Eucalyptus globulus and Diplotaxis harra were the richest with 77 and 75%, respectively. The physicochemical parameters of multifloral honey were intermediate compared to other honeys except for pollen density where the maximum was recorded with 14.83 × 10⁶ grains/10 g of honey. The concentration of proline was higher in honeys of A. gyzensis, Retama retam and the multifloral, which represent the same homogenous group according to Tukey's test. Whereas the honey dominated by Fabaceae sp. pollen was the least rich in proline with only 14 mg/kg of honey. All ANOVAs revealed very significant differences (P < 0.001) between floral origins for all honey parameters, where homogeneous groups of Tukey's test differ between honeys from one parameter to another (Table ​(Table11).

Antibacterial activity of honey according to floral origins

Overall, the antibacterial action of Saharan honeys differed from one bacterium to another. E. coli was the most sensitive species with a mean of inhibition diameter = 10.1 ± 4.7 mm (range: 0–24.9 mm) for all floral origins combined, while C. perfringens was the least sensitive with a mean activity of 3.9 ± 5.4 mm (range: 0–21.7 mm). The honeys tested against B. subtilis and S. aureus indicated an intermediate antibacterial activity between the two previous species with a mean = 8.0 ± 5.7 and 9.7 ± 1.5 mm, respectively.

Considering the floral origin of honeys, Fabaceae-pollen-based honey was the most effective with a mean of total activity = 13.5 ± 4.7 mm (min = 8.2, max = 21.7 mm). The activity of this type of honey was greater against B. subtilis (mean = 20 mm) and E. coli (mean = 15 mm). Other types of honeys showed moderate antimicrobial activities, with the following descending order: multifloral honey (9.1 ± 4.1 mm), P. persica (8.9 ± 7.6 mm), Ziziphus lotus (8.8 ± 5.1 mm), D. harra (7.2 ± 4.3 mm), A. gyzensis (6.9 ± 6.5 mm), E. globulus (5.5 ± 5.1 mm), and R. retam (5.3 ± 5.5 mm).

The use of trimethoprim-sulfamethoxazole as positive control against the strains of reference revealed inhibition activities of 24 mm for E. coli and 24.3 mm for S. aureus.

At the level of bacteria, all floral origins of honey showed an antimicrobial activity against S. aureus but with rather similar reactions (9–10.5 mm), except with P. persica-based honey, whose activity was only 6 mm. The bacteria E. coli experienced a greater inhibition effect when treated with honey of Fabaceae sp. (15.0 mm), Z. lotus (12.3 mm), and multifloral honey (11.6 mm). Whereas, a large variation in antibacterial activity of honeys was observed with both bacteria B. subtilis and C. perfringens. For B. subtilis, the antibacterial activity was higher with Fabaceae sp. honey (20 mm), but it was zero with those of E. globulus and P. persica. Similarly for C. perfringens, it was resistant toward honeys dominated by pollen of A. gyzensis, D. harra, and R. retam, while its growth was greatly reduced under treatment based on honey of P. persica (Figure ​(Figure22).

Box plots displaying the variation of the average values (•) and quartiles of antibacterial activity (expressed via inhibition zone) among the floral origins of honeys collected from the Sahara Desert of Algeria. (A, Bacillus subtilis; B, Clostridium perfringens; C, Escherichia coli; D, Staphylococcus aureus).

The assessment of antimicrobial activity of different antibiotics were determined against the two reference strains by measuring diameters of the inhibition zones. E. coli and S. aureus were clearly sensitive to all tested antibiotics. The Sulfamides (Trimethoprim-sulfamethoxazole, Clindamycin, and Fusidic acid) and Aminozides antibiotics (Gentamicin) were the most active antibiotics against E. coli.

Influence of honey parameters on antibacterial activity

Modeling the synergistic antimicrobial effects of honey parameters revealed that the inhibition zone “antimicrobial activity” was negatively associated with the bacteria C. perfringens and E. coli (P < 0.001 and P = 0.002, respectively). Whereas the variation of that activity was not significantly related in B. subtilis and S. aureus. The GLMM indicated that antimicrobial activity was higher (P = 0.003) in honeys dominated with Fabaceae sp. pollen compared to the other floral origins, where the diameter of inhibition zone significantly decreased when bacteria were treated respectively with multifloral honey (P = 0.049), D. harra (P = 0.030), R. retam (P < 0.001), P. persica (P < 0.001), E. globulus (P < 0.001). The inhibition zone was not significantly associated with honey of Z. lotus (P = 0.345) (Table ​(Table22).

Table 2

Generalized linear mixed model (GLMM) testing the effects of physicochemical parameters of Saharan honeys from different botanical origins on pathogenic bacteria (Akaike information criterion = 2071.5).

CI, confidence interval; SE, standard error; P, p-value; Sig, significance codes;

ns: not significant: P > 0.05.

Regarding the physicochemical parameters of honey, the statistical model demonstrated that the antimicrobial activity increased with increasing water content (P = 0.040); whereas it significantly decreased with increasing electrical conductivity (EC) (P = 0.007). Moreover, the antimicrobial activity was positively correlated with reducing sugars (P = 0.036). The rest of parameters have no significant effect on the variation of inhibition zone diameter (Figure ​(Figure3,3, Table ​Table22).

Effect graphs constructed based on the generalized linear mixed model “GLMM” testing the effects of physicochemical parameters and floral origins of Saharan honeys on four bacteria species. The antibacterial activity of honeys was expressed via the clear inhibition zone.

The interaction of the two factors “Bacterial strains × Floral origin” showed that the antimicrobial effect of Saharan honeys against C. perfringens and E. coli was deemed positively related to honeys originated from E. globulus, P. persica, R. retam, Z. lotus, and multifloral origin. While the inhibition zone in S. aureus was associated negatively with Fabaceae sp. but positively with honeys of E. globulus, R. retam, and P. persica (Table ​(Table22).

The LR test of the GLMM revealed that there was a highly significant effect of the bacterial strains, floral origins and their interaction “Bacterial strains × Floral origins” (P < 0.001) on the variation of antimicrobial activity of Saharan honeys (Table ​(Table3).3). Moreover, water content, EC, and reducing sugars concentration in honey significantly affected the antimicrobial activity. However, the effects of pH, HMF, total sugars, pollen density, and proline content were not significant (P > 0.05).

Table 3

Modeling the effects of physicochemical parameters of honeys with different floral origins and tested bacterial strains on the antimicrobial activity of honeys collected from the Sahara desert of Algeria.

Sum Sq., sum of squares; Df, degrees of freedom; F, F-statistic; P, P-value; Sig, significance codes;

ns, not significant: P > 0.05.

Discussion

This essay showed that both physicochemical properties and pollen composition of Saharan honey differ depending on their botanical origins. Thus, according to these two characteristics the antibacterial activity of these honeys varies between the tested bacteria. This may be explained by the fact that the antibacterial activity of honey essentially depends on the type of flowers from which bees gather nectar (Allen et al., ). But also the sensitivity/resistance of study strains influences that activity (Shahid et al., ), as it is the case of E. coli which reacted with high antibacterial activity values for both study honeys and antibiotics.

The significant variation in antimicrobial activity among the bacterial strains is assigned to the specificity of each bacterium, which reacts differently to honey parameters. According to Zaika (1988), Gram-positive bacteria are more resistant to essential oils than Gram-negative bacteria. This statement was however not confirmed by honey-related studies. Nevertheless, our honeys showed a higher antibacterial activity against E. coli, a Gram-negative bacterium, compared to the other three Gram-positive bacteria. Indeed, S. aureus resisted to several antibiotics so the resulted inhibition zones by honeys were slightly lower compared to those of E. coli. Though testing quantitatively a variety of each group species with a good number of isolates provides determine relevantly the activity trend of honeys. Despite that, our results are in agreement with the investigations of Shamala et al. (2002) in which honey showed a significant antibacterial activity against E. coli either in vitro and in vivo conditions. Additionally, the marked sensitivity of study bacteria to certain types of honey (e.g., with the origin of Fabaceae sp.) is probably linked to medicinal properties of the dominant flower from which honey was produced. These effective honeys can be used as an alternative to fight against some resistance strains.

Our results revealed that the overall antimicrobial activity increases with the content of water in honey. These findings are similar to those of honeys from New Zealand, where the antibacterial activity was found to be more effective at low concentrations of honey (Molan and Russell, 1988). This assumes that the antimicrobial activity of our honeys depends on the content of endogenous hydrogen peroxide, which is the main antibacterial agent in honey (Morse, 1986). In fact, the antibacterial potential of hydrogen peroxide results from the action of these highly reactive oxidizing molecules, which play the role of a “cleaning agent” attacking the cell membrane of microorganisms by producing free radicals that induce cell destruction. The latter cause damage to cell membrane lipids, isolating the cell, inhibiting the entry of nutrients, and the removal of waste material; thus triggering gradually slow death of the microorganism (Lu et al., ; Brudzynski, ; Erejuwa et al., ). Since water is essential to the oxidation process, hydrogen peroxide is typically produced in immature honeys in which water content is high. While in a ripe honey in which moisture content is low, glucose oxidase remains inactive so the oxidation process are limited. Thus, the honey contains a small amount of hydrogen peroxide insufficient to prevent bacterial growth unless water content increases (Bogdanov and Blumer, 2001). That perfectly explains the positive correlation between honey moisture content and the antimicrobial action obtained in the statistical model.

Furthermore, other molecules grouped under the name of non-peroxide inhibins can be the cause of the antibacterial action of honey; their origin is also the subject of lively discussions (Mavric et al., ; Mandal and Mandal, ). Some studies state that these molecules are of plant origin, while others declare that are added by bees when developing honey. The role of non-peroxide inhibins, often underestimated, is very important as they are at a large extent: insensitive to heat and light, and remains intact after honey storage for long periods (Bogdanov, 1984; Reybroeck et al., 2014).

The antimicrobial activity of Saharan honeys was more effective against bacteria when the honey has low EC. The latter is linked to the ionizable material content in which the mineral matter represents the essential. EC depends on the nature of the dissolved ions and their concentration (Rejsek, 2002), which in turn is linked to the botanical origin of honey and indirectly linked to various environmental conditions, including edaphic factors upon which melliferous plants substantially depend (Thasyvorlor and Manikis, 1995). This corroborates with our findings where the antimicrobial activity varied significantly between botanical origins, which are behind the significant change in EC.

Furthermore, according to Bonté and Desmoulière (2013), potassium salts represent almost half of honey inorganic materials, but there is also calcium, sodium, magnesium, copper, manganese, chlorine, sulfur, silicon, iron, and more than 30 trace elements. Minerals play an important role in biological systems, but can also cause harmful effects if their inputs exceed the recommended amounts (Tuzen and Soylak, 2005). Our findings imply that the factors that may affect the antibacterial activity of honey can have a redundant activity, or be mutually dependent, or even have antagonistic or synergistic activity against different bacterial species (Thasyvorlor and Manikis, 1995; Wahdan, ).

The positive correlation of the antimicrobial activity with reducing sugars is associated to the osmotic effect generated by high sugar concentration in honey. As honey is hypertonic, and due to the action of simple sugars on water contained in bacteria, it causes the lysis of the bacterial membrane, inhibition of the growth and then death of the microorganism (Couquet et al., 2013). For this parameter, our results are similar to those reported in Mandal and Mandal ().

Since the antibacterial activity was high with honeys originated from Fabaceae sp. P. persica, Z. lotus and multifloral honey, we speculate that these honeys contain a high content of hydrogen peroxide and even other non-peroxide inhibins such as lysozymes, flavonoids, aromatic acids, and volatile substances (Wahdan, ; Brudzynski, ; Montenegro and Mejías, ). Therefore, the use of sophisticated and complementary techniques enables detecting and quantifying accurately these compounds in different botanical origins of honey (Alzahrani et al., ). For example, for the analysis of phenolic acids and flavonoids, which depend on the floral origin of honey, the use of modern conventional techniques such as GC-MS and LC-MS allows to determine the floral origin of honey having the more effective use as an antimicrobial agent (Bertoncelj et al., 2007; Boukraâ, 2013). In addition, NMR allows the analysis of complex mixtures of natural products such as honey and thus the identification and quantification of various families of compounds regardless of their structure (Tiwari et al., ).

Conclusion

This assay shows that Saharan honeys have multiple floral origins, which are causing differences in their physicochemical and pollinic characteristics. The GLMM revealed that antibacterial effect increases with increasing water content and reducing sugars in honey, while it decreases with increasing EC. These three parameters are the more relevant parameters that were correlated with antibacterial activity that differed significantly from one bacterium to another. E. coli was the most sensitive species while C. perfringens was the least sensitive. Honeys tested against B. subtilis and S. aureus indicated intermediate antibacterial activity.

In light of floral origins, our results suggest that Saharan honeys with the floral origin of Fabaceae sp. have a higher detrimental effect on bacteria compared to other spontaneous Saharan species, known for their common uses in traditional medicine such as Zizyphus lotus or D. harra. Most likely, this returns to the source of nectar collected from these species well-adapted to arid conditions. Yet, several factors, particularly the ecological ones, can affect the melliferous plants; thus additional research are required to fill the scientific gaps in this still virgin field of research in drylands.

Author contributions

HL designed the study, collected honey samples, carried out all laboratory experiments (antibiotic tests, honey antibacterial assays, physicochemical, and pollinic analyses), and drafted the manuscript. LB contributed in the pollinic analysis. SB gave technical support and conceptual advice. TM helped in drafting and revision of the manuscript. SH and MM performed physicochemical analyses on honey samples. RH conducted the experiment of honey antibacterial tests. HC conceived the paper, analyzed and modeled statistically data, wrote and revised the article.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors wish to thank Dr. Ken Norris (Institute of Zoology, Zoological Society of London) for helping us in mastering data analysis using mixed-effects models “GLMM” in R. Thanks are extended to Prof. Ishag Adam (Faculty of Medicine, University of Khartoum, Sudan) for his constructive comments on an earlier version of the manuscript.

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Biologie Vegetale De Boeck Pdf Viewer 2017

Associated Data

Abstract

An experiment explicitly introducing learning strategies to a large, first-year undergraduate cell biology course was undertaken to see whether awareness and use of strategies had a measurable impact on student performance. The construction of concept maps was selected as the strategy to be introduced because of an inherent coherence with a course structured by concepts. Data were collected over three different semesters of an introductory cell biology course, all teaching similar course material with the same professor and all evaluated using similar examinations. The first group, used as a control, did not construct concept maps, the second group constructed individual concept maps, and the third group first constructed individual maps then validated their maps in small teams to provide peer feedback about the individual maps. Assessment of the experiment involved student performance on the final exam, anonymous polls of student perceptions, failure rate, and retention of information at the start of the following year. The main conclusion drawn is that concept maps without feedback have no significant effect on student performance, whereas concept maps with feedback produced a measurable increase in student problem-solving performance and a decrease in failure rates.

INTRODUCTION

Mastery of science requires understanding a wide range of different concepts, most of which are specific to the particular subject. Within a discipline, however, the essential concepts generally seem to have broad consensus. For example, when course outlines for a random selection of first-year cell biology courses are compared, the concepts appearing in the course outlines are remarkably consistent, and correspond well to the basic concepts inventoried as part of a recent hierarchically ordered biology concept list (). Cell biology concept inventories, following along the lines of concept inventories in other areas such as physics (Hestenes et al., 1992), are important because they focus attention on the concepts that should be included during course development and can eventually be used to develop standardized tests to ensure that subject mastery has been met according to national scales.

The selection of concepts and preparation of concept-based course material represents only part of the teaching and learning equation. In fact, the desired end point of a course, the acquisition of new knowledge by the students, may be facilitated but is not a direct function of how the course material is organized. In contrast, what students learn can be profoundly influenced by two other factors, the use of strategies (or active learning) and a student's motivation to learn the material. The use of learning strategies is a part of the constructivist perspective, where learning is proposed to involve the active construction of knowledge by a student (). There are many types of learning strategies, classified in general terms into cognitive, metacognitive, emotional, and management categories, and all can have some impact on student learning (Tardif, 1997). However, the cognitive concept mapping (CMAP) strategy of Novak (Novak and Gowin, 1984) seems ideally suited to cell biology, because it is a course already organized by concepts. In the preparation of a CMAP, students are asked to select important facts, organize them, and integrate them into a complete picture. The CMAPs are inherently personal representations of what have been learned, can be used for evaluation purposes (McClure et al., 1999), and have been demonstrated to be an effective learning strategy (Horton et al., 1993; ).

The motivation of students is often ignored during course preparation and delivery but can be a potentially powerful lever to influence student learning. A motivated student effectively uses learning strategies and is willing to make an effort to learn even without an initial success (Tardif, 1997). Some motivational forces are internal to the student and can be learned, such as an effective use of cognitive and metacognitive strategies (Viau, 1994). Other motivational forces are external to the student and can include help from the professor or from other students (Viau, 1994). However, among the most important motivational forces are the perceived value of the material (“why do I do it?”), the perceived ability of the student (“can I do it?”), and the perceived control over the process (“does effort produce results?”). A sense of control over the final outcome of their own learning process is important (Tardif, 1997): Students who have the impression that nothing they do will alter the result of the learning process, or who attribute success to good luck and failure to bad luck, or who see the pedagogy and didactic practice of the professor as the sole determinant of success or failure, will make little effort to contribute to their own learning. In the context of a concept-based cell biology course, the perceived value or significance of the material can be targeted by reference to the same material covered in concurrent courses, to a demonstration that the knowledge is essential for professional expertise, or to its relevance with respect to social issues. Perceived ability can be targeted by careful choice of graded problems presented to the students. Here, accurate estimation of the difficulty of the problems is paramount, as problems that are too easy preclude significant learning, whereas problems that are too difficult frustrate rather than stimulate. Perceived control by the students over their academic performance represents the aspect directly targeted by explicit teaching of learning strategies. Ideally, this teaching should be accompanied by a demonstration for the students that learning strategies do indeed work.

In the spirit of scientific teaching (), the experiment described here reports an impact assessment of implanting a concept mapping learning strategy into a concept-based introductory cell biology course. The methods used are applicable to large classrooms and do not require teaching assistants.

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COURSE DESIGN

Context

BIO1153 is a three-credit-long, first-year undergraduate cell biology class given to ∼ 220 students at the University of Montreal during their first semester. Students enter the 3-yr university biology program after 13 years of pre-university schooling from a number of different schools, although many use the same text (Campbell, 2001) for biology instruction. This introductory cell biology class is a required course, and the content is relevant for roughly half of the other courses (particularly biochemistry, genetics, molecular biology, and physiology) offered in the department. All formal instruction is in French, but translated versions of most standard cell biology textbooks are available. BIO1153 explicitly references the textbook of Alberts et al. and the related problems book (Alberts et al., 2004; Wilson and Hunt, 2004). The course is expected to require roughly 2 h home study for every hour of class time, although this level of effort may not always be possible for all students, as many students work at least part-time in addition to their university studies.

The choice of material included in this first-year course is also influenced by the fact that a second-year cell biology course is available. The second-year course focuses primarily on signaling, cell cycle, and development, so none of these subjects are treated extensively in the first-year course. It is also noteworthy that the same professor is responsible for both first-year and second-year courses, limiting the time that the professor can devote to grading and assisting students.

Course Objectives

BIO1153 has four goals:

1. to expose students to the basic vocabulary and concepts of cell biology, with particular attention to the relationship between structure and function;

2. to develop critical-thinking skills, with emphasis on the correct interpretation of scientific data;

3. to develop a social conscience in issues where cell biology impacts directly on society; and

4. to explicitly expose students to cognitive and metacognitive strategies in learning and develop facility with the concept mapping strategy.

The learning outcomes expected when these teaching goals have been met are:

1. participants will respond adequately to standard multiple-choice questionnaires testing the basic vocabulary and concepts of cell biology;

2. participants can correctly interpret the results of scientific data given in the context of a problem;

3. participants can understand and discuss the scientific basis of current social issues in cell biology; and

4. participants are able to effectively use concept mapping as a learning strategy.

Preparation of Modules

To achieve the course goals, the course material has been constructed as a series of eight modules, each of which deals with a particular subject and contains between five and 14 concepts. Each concept is designed to take up roughly 15 to 25 min of lecture time and has a standard format typically consisting of nine elements:

• Concept. An explicit statement of the concept.

• Background. A reactivation of previous knowledge.

• Idea. A statement of the basic idea or an analogy to illustrate it.

• Example 1. A concrete example of the concept.

• Example 2. A counterexample (if possible).

• Example 3. Another example of the concept.

• Summary. A resume of the material.

• Questions. A series of questions to test comprehension of the concept.

• Problem. A problem with real data to be solved in class.

This approach is derived from a previously described instructional design method (Gagne, 1977). The goal of the presentation format is to facilitate the construction of new knowledge by making explicit the basic concept and the foundations upon which it lies. The alternation between example, counterexample, and example is performed to allow the students to decontextualize then recontextualize the concept. The end-of-concept questions are very general and are provided to allow the students to evaluate their comprehension of the concept. Lastly, the problems are meant to illustrate the concept using real experimental data or issues in cell biology. These problems are read by the students before the lecture and then discussed in class in the light of the lecture material. The problems used were derived from a standard text of cell biology problems (Wilson and Hunt, 2004), although in many cases the precise question asked has been modified to take into account the students' ability and coherence with the concept in which it is embedded.

The conceptual structure of the modules is formulated in a manner that roughly recapitulates the conceptual order found in the text (Alberts et al., 2004). The Massachusetts Institute of Technology hierarchical concept framework () has also been partially incorporated where possible to facilitate the preparation of concept maps from the material.

The decision to incorporate real-data problems into the concept presentation framework was based on three factors. First, problem solving provides a break in the standard lecture format by allowing students to discuss the subject with their colleagues in class. Second, the problems provide an alternative illustration of the concept that the students have to come to on their own. Lastly, the solution of problems during the semester not only increases student interest in the course but increases performance (). The integration of the problems was expected to motivate the students into a greater intellectual investment when assimilating the course material.

The concepts each require roughly 20 to 25 min of lecture time, after which ∼5 min class time is allotted for either resolution of a problem related to the concept, an in-class demonstration of the concept, or to solicitation from the students thoughts of how the concept may impact society. There is thus a change of pace and style at regular intervals that is expected to maintain interest and motivation. Problems, when included in the concepts, are taken from the accompanying problems book (Wilson and Hunt, 2004). Class demonstrations involve simple “experiments,” such as passing the beam from a laser pointer through electron microscopy grids with different grid spacings to show that diffraction patterns become more spread out as the grid spacing decreases. Solicitation of student opinions can range from their thoughts on Botox to whether insurance companies should have access to their genome sequence. A detailed plan of the course (provided in Supplemental Material 1) shows the selection of concepts, examples and accompanying problems, strategies, and discussion items.

Preparation of Students for Concept Mapping

CMAP is a learning strategy developed by Novak in the 1980s (Novak and Gowin, 1984), and involves selecting key ideas or concepts from the material, organizing the ideas into a hierarchical structure, and then linking the ideas together by verbs or prepositions. This strategy allows a student to place new material into a knowledge structure compatible with previous knowledge in a manner that makes it accessible in other situations. However, the strategy must be taught explicitly to the students before it can be used. The first step in implanting the strategy of concept mapping (Novak and Gowin, 1984) requires showing the students what the strategy entails, how it will work in the context of the course, and why it is important. During the first several minutes of the first lecture, the students are told that:

• they will be required to hand in visual summaries called concept maps (CMAP) for each module;

• each CMAP package will include a team CMAP and an individual CMAP from each member (team maps can be either a new construct or simply an individual CMAP that adequately reflects the material);

• the first lecture will include examples of CMAPs and a series of exercises designed to teach concept map construction; and

• points will be awarded for handing each CMAP package in on time.

The first example of a CMAP shown represents the course material that will be covered, and serves to both introduce the course and to demonstrate the three essential elements absolutely required for efficient utilization of the concept mapping strategy:

• the selection of key words or concepts;

• the arrangement of the concepts hierarchically on a page; and

• the connection of the concepts by lines labeled with verbs or prepositions.

The first lecture provides practice for each of the three essential elements separately and then together. These practice times are given at the end of concepts, in place of the problems that normally end the concept in the other modules. To provide practice of the first element, the students are asked to select five or six key words that best reflect or describe the material covered. After presentation of the concept, they are allowed to discuss their choices with their immediate neighbors for several minutes, after which the obvious choices are presented and discussed. For practice of the second element, a predetermined series of six key words is given at the end of a concept, and the students are asked to arrange then hierarchically on a piece of paper. Again, discussion is encouraged and the obvious organization is presented and discussed. For practice of the third element, a series of six key words already arranged hierarchically with connection lines lacking labels is provided, and the students are asked to label each connection line. Again, after several minutes the obvious choices are presented and discussed. The last exercise, in which the students are asked to perform all three tasks sequentially, is given for the last concept of the lecture. At the end of the lecture, a summary CMAP of the first module is presented and discussed. This example also illustrates a grading scheme for concept maps that gives 1 point for each correctly identified vertical link, 5 points for each valid hierarchical level, and 10 points for each correctly identified horizontal link (Novak and Gowin, 1984). This scheme is important as it underscores the importance of hierarchical concepts and the ability to make links between different areas of the construct.

The homework is scheduled so that students prepare their individual CMAPs from the module before the class, and have a full week to consult with their team and prepare the consensus team CMAP; individual and consensus maps are handed in together at the start of the next lecture.

Preparation of Students for Problem Solving

No training for solving problems is given during the first lecture because of the emphasis on CMAP training. However, during subsequent lectures, strategies appropriate for problem solving are provided during the in-class discussion of the problem. The problems are given in the form of text with a figure and figure legend. Experience has suggested that the most useful strategy in problem solving is given as a series of steps:

1. scanning the text to identify the question to be answered;

2. writing a description of what is understood in the figure;

3. attempting to answer the question using the description of the figure;

4. identification of any elements in the figure that are not clear;

5. scanning the text for explanation of those elements specifically.

This strategy is meant to make the students more comfortable with the problem solving in an exam situation.

Observations During the Experiment

The first lecture session appeared to be effective in terms of student training. However, formation of the teams was problematic, perhaps exacerbated by shyness and by a growing tendency for students to work in addition to their studies (which severely constrains their availability). In the first year of implementation, teams were suggested but not made mandatory. Roughly three-quarters of the students chose to produce individual CMAPs rather than work in teams, and these students were thus unable to obtain feedback on their CMAPs. During this year (2004), no impact of the CMAP was observable on any measurable parameter of student success (see below). Consequently, teamwork was made obligatory during the following year (2005). Teams were formed randomly using an automated function in WebCT, the university platform for e-learning. Facilitating teamwork is time consuming to the instructor. Difficulty in contacting partners, and student withdrawal from the course both cause problems that have to be solved by the instructor. The method chosen in 2006, in which students were asked to make their own teams, thus appears to have the best cost-per-benefit ratio.

An additional feature of WebCT is that students can send messages either to a specific individual or to a group forum. These online communications can be monitored by the course administrator, and provide a valuable window into problems experienced by the students during the course. With respect to the CMAPs, some discussion in the forum centered around whether or not it was worth investing time in preparing the CMAPs, as several students noted grades were assigned for completion of the maps and did not reflect their quality in any way. The counterargument, slightly more prevalent than the first, noted that the CMAPs were personal study aids and would not contribute to success on exams unless they were done well.

A useful mapping program (CMAP tools) compatible with both PC and Mac is distributed free of charge by the Institute for Human and Machine Cognition (http://cmap.ihmc.us). The computer version is superior to pen and paper in that the maps can be readily reworked, and an advanced version of the program can accommodate multiple users working on the same map from different locations. Although none of the student teams chose to use the team map function, more than 95% of the students did use this tool in preparing their individual CMAPs.

CMAP Evaluations

Evaluating an individual CMAP requires between 20 and 30 min. Thus, given the class size and the total number of maps asked of the students (seven over the semester), extensive evaluations of all maps during the semester is not possible. This is the principal reason for peer group evaluation of the maps, as otherwise no feedback about the validity of the map can be provided to the student.

It is, however, possible to evaluate the maps after the course, and although this evaluation neither helps the students nor contributes to their grades, it does provide a measure that can be tested for correlation with other measures of student performance. CMAPs from two of the modules were selected for analysis: CMAP2, which requires extensive hierarchical ordering, and CMAP6, which lends itself particularly well to horizontal links. Before the students constructed CMAP2, they were informed that hierarchical ordering was an especially important element for proper representation of the material. Each individual student CMAP was then scored as to whether or not hierarchical ordering was present; for those in which hierarchical ordering was present, two different patterns were noted, and these two patterns were scored independently. Before the students constructed CMAP6, they were asked to pay special attention to the formation of horizontal links. As for CMAP2, the individual CMAP6 was scored as to the presence of horizontal links, and when present the type of pattern observed. In the year where both individual and consensus CMAPs were requested, both were scored.

In a more detailed analysis of the CMAPs, 30 students were selected from the class of more than 200 on the basis of their final exam performance (the 10 individuals with the highest grades, the 10 with the poorest grades, and 10 individuals with average grades). For each of these 30 students, two maps (CMAP2 and CMAP6) were evaluated quantitatively using the numerical scheme previously described (Novak and Gowin, 1984).

Evaluating Student Performance and Impact of the CMAP Strategy

Student evaluations for grading use both multiple-choice exams (GRE, or graduate record exam style) and open-ended problems (as found in the problems book of Wilson and Hunt, 2004). Multiple-choice questions are computer graded, and the five or six open-ended problems on the midterm and the final exam (20% of the exam), respectively, are evaluated by the instructor. A student's final grade is the sum of the midterm, the final exams, and a total of 20% given for production of CMAPs. Note that grades are awarded for production of the concept maps and are not influenced by the quality.

To assess the impact of implementing the CMAP strategy, both final examination results and student failure rates were taken as measures of student success. The computer-graded multiple-choice questions and hand-graded answers to the problems in the final exam were noted separately for each student. Student failure rate was scored as the number of students whose final grade was below 50%. Note that the CMAP project began in 2004, so student final grades in 2003 reflect only midterm and final exam scores, whereas in 2004 and subsequent years, the final grade also includes the points awarded for CMAP production.

At the end of the school year (last class), students were also asked a series of multiple-choice questions directly soliciting their opinions on the use of the concept mapping strategy and the inclusion of the problems as a learning aid. The replies are anonymous as questions were given to the students in the context of the yearly course evaluations (the professor is absent). These results were compiled by the same university service that processes the other questions on the evaluation.

An additional evaluation involved a short multiple-choice questionnaire given to students enrolled in a second-year cell biology course. The majority of the students in the second-year course took BIO1153 the previous year, and the questionnaire was intended to estimate the retention of material given during the first-year course. The results from the second-year students during academic year 2006 were not included as the first-year class for these students was interrupted by a labor dispute at the university during which three courses (of 12) were not given and the CMAP experiment was abandoned.

Analysis of the Impact of the CMAP Strategy

The collected data, including student grades during the exams (with problems and multiple-choice questions collected separately) and the CMAP scores for the 30 selected students, were entered into an Excel spreadsheet. In the first analyses, the effect size of Horton (Horton et al., 1993) was calculated using the formula:

Means and standard deviations were calculated from the data using the preset programs in the spreadsheet. The problems and multiple-choice questions were maintained separately for this analysis as multiple-choice questions were expected to test lower levels of thinking (knowledge and understanding) as defined by Bloom's taxonomy (Anderson and Krathwohl, 2001), whereas the problems were expected to test higher levels of thinking (application and analysis).

In a second series of analyses, a possible correlation between CMAP scores and final grades was tested. For this analysis, the CMAP score for an individual was plotted as function of the exam score, a straight line drawn through the data points by linear regression, and the degree of correlation tested by calculation of the coefficient R².

A last test was performed with the 2006 group to gauge their performance with respect to national norms. To accomplish this, 28 multiple-choice questions covering the course material were selected from old GREs in biochemistry and molecular biology that are supplied to students as a study guide and preparation for taking the exams (www.ets.org/Media/Tests/GRE/pdf/BioChem.pdf). These practice exams are useful because they contain not only answers but also the national average of successful responses for each question. The means and standard deviations of the success rate of the class were calculated and compared with those derived from the national average for all questions combined. In addition, the success rate for each question individually was tested for a possible correlation by drawing a linear regression line through the data points of class score plotted as a function of national average score.

RESULTS

The rationale for this study is displayed as a concept map in Figure 1. The concepts selected as course material represent the bulk of the information that must be assimilated by the students, and this is expected to be more efficient when students are motivated and when they are effectively using learning strategies. Motivation for map construction is affected by allotting points for completed maps and by the constant feedback students obtain about their maps though team consultation.

Concept map of the project and its theoretical foundation.

During the first 15 min of the first class, students were asked to complete a short questionnaire to survey the general level of background knowledge in cell biology (10 multiple-choice questions on material found in the text used for most pre-university biology courses). Students were asked to select all applicable answers to a specific question, and on average, selected right answers 67% of the time and wrong answers 11% of the time. This score is similar to the average cell biology grade at the end of the semester (72%) so it may reflect the acquisition of basic knowledge capacity of the class. Students were also asked to list any or all learning strategies they typically used. The responses were classified into real learning strategies or not based on a list of strategies (provided in Supplemental Material 2) compiled from a published work (Barbeau et al., 1997). In contrast to their performance on biology context questions, only 30% of the 236 responses represented real strategies (Table 1). Indeed, the majority of the responses erroneously identified simply reading and rereading the course notes as a learning strategy. This level of awareness suggests that explicit instruction in strategic learning could indeed be beneficial. Lastly, students were also asked why they were enrolled in the biology program, with 51% expressing a desire to work in a biology-related field, although a significant percentage (38%) indicated biology was a stepping-stone to a medical school application.

Table 1.

Most students do not use efficient learning strategies

Responses elicited by an anonymous survey of study strategies used were grouped into similar types and the types classified as either cognitive learning strategies or not based on a list of learning strategies (provided in Supplemental Material 2) compiled from the literature (Barbeau et al., 1997).

The impact of introducing the CMAP strategy into the class might be expected to depend on the degree to which it is used effectively. Indeed, a problem with implementation was detected during the first semester of the CMAP experiment (2004) by simply counting inadequate CMAPs. For example, during construction of a concept map describing different microscopes (CMAP2), the students were expected to organize what they had learned. This module lends itself particularly well to the use of hierarchical structure in the maps. However, the most frequent type of CMAP produced lacked hierarchical order (Table 2). An example of this type of construction (Figure 2A) is akin to a spider, with all the different microscopes emerging like legs from the same body. To a lesser degree, hierarchical order was found among the maps in one of two different patterns (Table 2), one in which the microscopes were ordered by the wavelength of incident radiation (Figure 2B) and the other which differentiated between microscopes in which light passed through or did not (Figure 2C). An example of an instructor CMAP of the material is also presented for comparison purposes (Figure 2D).

Table 2.

Feedback is important for adequate conceptualization

Concept maps from module 2 (microscopy) and module 6 (energy metabolism) were grouped into types (examples of each are given in Figures 2 and 3, respectively), and the number of students producing maps of each type tabulated. Data for 2006 are given both for the individual maps (Individual) as well as for the total number of individuals supporting consensus maps (Groups).

Student concept maps for the microscopy module illustrating common patterns. Pattern types observed for CMAP 2 (microscopy) include (A) all microscopes treated equally (no hierarchical distinctions), (B) distinction between classes of microscope made on the basis of diffraction, and (C) distinction between classes of microscope made by the wavelength of radiation. An instructor CMAP is supplied as an example (D).

A second example that illustrates problems in the effective use of the mapping strategy can be found in the module treating energy conversion in the mitochondria and chloroplasts (CMAP6). There are many similarities between the two organelles, and thus this map was expected to be particularly appropriate for the formation of horizontal links. However, the majority of the maps simply portrayed the two organelles as two separate hierarchies with no connecting links (Table 2). Examples of the patterns found include those with no links (Figure 3A), as well as those with horizontal links formed either on the basis of the organelle's structure (Figure 3B) or by how energy is converted (Figure 3C). An instructor CMAP is again presented as example (Figure 3D).

Student concept maps for the energy metabolism module illustrating common patterns. Pattern types observed for CMAP6 (energy metabolism in mitochondria and chloroplasts) include (A) mitochondria and chloroplasts treated separately (no horizontal links), (B) horizontal links between mitochondria and chloroplasts made by structural similarities, and (C) horizontal links between mitochondria and chloroplasts made through the form of energy used. An instructor CMAP is supplied as an example (D).

For the repetition of the experiment in 2006, students were shown examples of CMAP2 without appreciable hierarchy, and of CMAP6 without horizontal links present, and requested to pay special attention to these aspects when constructing their own maps. Given this prompting, it was somewhat surprising to recover so many individual maps without the desired characteristics, although there was improvement over the 2004 responses, particularly for CMAP6 (Table 2). One interpretation of this poor performance is that the students were unaware to what extent their individual maps corresponded to an inadequate conceptualization. This view is supported by the observation that when students were asked to decide on a consensus map as a team, the number of individuals represented by the team's choices shows further increase in the number of adequate maps (Table 2). This result suggests that peer-level feedback is an effective method to ensure a higher quality of maps. We conclude that feedback is an important component to successful implantation of the CMAP strategy.

To determine the impact of CMAP construction, the performance of the students on the final exam was also evaluated as a potential indicator of a positive impact. For the exam taken as a whole, there was only a slight improvement during 2006 compared with previous years, as is found for the final exam of a related course taken concurrently by the same students (Figure 4A, white bars). However, when the multiple-choice questions and problems are considered separately, a marked difference becomes apparent. Student success with multiple-choice questions remains generally stable between 2003 and 2006 (Figure 4A, gray bars). However, the success rate with problems increases dramatically during 2006 (Figure 4A, black bars). Because five of the six problems were the same between 2004 and 2006, the increase is unlikely to have resulted from a selection of easier problems. It is also unlikely to result from increased training during the semester, as the basic course outline remained unchanged. It is thus tempting to hypothesize that the construction of more adequate CMAPs resulted in increased problem-solving capability. To test the significance of this increase, the effect size was calculated for 2006 in comparison to both 2003 and 2004 (Horton et al., 1993). The effect size is calculated from the difference between the experimental and the control mean divided by the SD. No difference between the experimental and the control would return a value of zero, while a difference equal to the SD would return a value of one. For comparison purposes, the reported effect size attributable to CMAP was 0.42 ± 0.18 (n = 14) for science in general and 0.66 ± 0.25 (n = 9) for biology in particular. The effect size calculated for the problems during 2006 as the experimental year was 0.72 and 0.73 using 2003 and 2004 as the control year, respectively.

Assessment of student performance. (A) Grades for multiple-choice questions (gray bars) and problems (black bars) for both cell biology and for a control course (white bars) for the 3 yr of the project. (B) Course failure rates for BIO1153 and a control course for 3 yr (the number of students was 234 in 2003, 224 in 2004, and 206 in 2006). (C) A comparison of grades in the control course with those obtained in BIO1153.

The increase in problem-solving ability and the resulting increase in the final grade led to a significant decrease in the number of students failing the course (<50% final grade; Figure 4B). Indeed, in 2006 only a single student did not pass. Although the final grades include the points given for completion of the CMAPs, these points are unlikely to be the cause of increased final grades, as the same points were allotted for map construction in 2004. It thus seems more reasonable to attribute the decreased failure rate to the observed increase in exam performance resulting from increased problem-solving capability (Figure 4A).

To determine the relationship between CMAP construction and academic performance, a selection of CMAPs were evaluated and the results plotted as a function of the final exam grade. A general correlation was observed for both CMAP2 and CMAP6 (Figure 5A), which becomes even more pronounced when the scores from both CMAPs are added together (Figure 5C). This is presumably attributable to the fact that these two CMAPs require different skills, one for determining hierarchy and the other for identifying horizontal links. This view is supported by the observation that there is little correlation between the scores of the two CMAPs themselves (Figure 5B).

Relationship between final exam results and CMAP scores. Thirty students were selected from the class to represent high (top 10), medium (around the mean), and low (bottom 10) final exam grades. (A) Concept maps for the microscopy (CMAP2, open circles, example of adequate hierarchy) and energy (CMAP6, closed circles, example of adequate horizontal links) for all students were scored and individual CMAP scores were plotted as a function of final exam grade. (B) CMAP scores plotted as a function of one another. (C) Scores from the two CMAPs were combined and plotted as a function of final exam grade.

To assess the impact of introducing the CMAP strategy on retention of basic cell biology concepts, a short multiple-choice test was given to second-year students during the first 15 min of the class (Table 3). The test given during 2004 serves as a control, as students taking the first-year course in 2003 were not exposed to the CMAP strategy. Students in 2004 used CMAPs inefficiently, as described above, and the standard test results were virtually identical to those from the previous year's students who did not use CMAPs. Students who took the first-year course in 2005 were not included in the analysis (this course was interrupted by a labor dispute at the university making interpretation of the second-year test results in 2006 difficult). However, when the test was administered to second-year students in 2007, where significant impact of the CMAP strategy was observed with problem solving during the first year, a significant increase in test scores was observed (Table 3).

Table 3.

CMAPs aid knowledge retention by second-year students

A 10-question survey of basic concepts covered in the first-year cell biology course (BIO1153) was given to students beginning a second-year cell biology course (students in 2007 took BIO1153 in 2006).

One concern for the interpretation of the experiment is that the general ability of the students in one year might be quite different from that in other years. Thus, improvement in performance might reflect improvement in students entering the course rather than an effect of the intervention. Two observations argue against this possibility. First, 28 questions were selected from past years' general biology and biochemistry GREs. These exams provide sample questions used in previous years for study purposes and list not only the correct answer but the national success rate. The questions selected were directly related to the course material studied during the year. The class average on GRE multiple-choice questions was 61 ± 21%, almost identical with the national North American average of 62 ± 17% for the same questions. BIO1153 students can thus be considered as a representative North American class. Second, the grade for a comparison course shows no change in year 2006 (Figure 4A).

A second concern for the general applicability of CMAP strategies related to the general perception of the strategy by the students. For example, the strategy is likely to be used effectively if the students perceive the strategy as useful and if the amount of work does not act as an impediment to its implementation. These aspects were tested by adding questions relating specifically to CMAPs to the standard course evaluation administered by the university. More than 90% of the students agree that the strategy is appropriate for keeping up to date and for making links between concepts, whereas more than 80% agree that the strategy prepares them for the exam and helps them understand the material (Table 4). Furthermore, more than 90% of students during the 2006 school year considered that the construction of individual maps and the validation of their maps in teams represented an adequate workload (Table 5).

Table 4.

First-year students at the end of the 2006 school year (135 respondents) were asked the degree to which they agreed with a series of statements referring to the CMAP strategy in an anonymous student evaluation.

Table 5.

CMAPs are perceived to represent an adequate workload by students

First-year students at the end of the indicated school year were asked the degree to which they agreed with the statement ″the workload of the course is adequate.″

DISCUSSION

One of the most important messages provided by these results is that effective use of concept mapping as a learning strategy must include a mechanism to ensure feedback to the students about the validity of their interpretations. In particular, the lack of any improvement in student exam performance in 2004 may well have been due to this lack of feedback on individual maps. This possibility is supported by the observation that student CMAPs in 2004 were generally characterized by poor organization and poor integration of the material into a coherent whole. For example, CMAP2 generally lacked proper hierarchical organization, while CMAP6 generally lacked horizontal links. These elements are actually more important than the concepts themselves, as indicated by their contribution to the scoring scheme developed for assigning numerical evaluations to different maps (Novak and Gowin, 1984). Furthermore, when the level of hierarchization and horizontal linking generally increased for the class of 2006 (Table 2), student performance on final exam problems did display a noticeable improvement (Figure 4).

The principal factor that was modified between 2004 and 2006 is the requirement for students to discuss their CMAPs with a team and to select (or make) a consensus map. This requirement imposes peer evaluations in the context of small teams to identify inadequate representations. Actually, as the students were requested to form their own teams, teams in this context should most probably be considered as “groups,” rather than teams whose composition is determined on the basis of student abilities (Michaelsen et al., 2004). Although the formation of true teams whose members have complementary skills might well prove to be more effective, it is difficult to see how team members could be assigned given that instructor access to student records is blocked.

The team evaluation of individual CMAPs was introduced to allow for feedback in large classes where instructor time is limited and individual maps cannot be corrected and returned to the students. By way of reference, in this study correcting the selected CMAPs used to derive the data in Figure 5 required ∼30 min each. Clearly, this is not a cost- effective method of student evaluation unless a suitable rubric could be designed and incorporated into an automated system. It must be noted, however, that even when required to produce individual and group CMAPs, the students themselves do not perceive this to be an unduly taxing workload (Table 5). Thus, given the learning gains indicated from increases in final grades (Figure 4) and the increased retention of information by second-year students (Table 3), the feedback-adapted CMAP strategy does appear to be an effective tool for student learning.

In addition to the critical value of peer evaluation, a second important message of this study is that the methods for evaluating the impact of an intervention and those used for assessing student performance must be coherent with the desired learning outcomes. For example, multiple-choice GRE-style exam questions might not be the most appropriate for evaluating the impact of a particular learning strategy. As shown here, the students do no better on multiple-choice questions after applying a CMAP learning strategy than they did in previous years without the strategy (Table 2). However, it cannot be concluded that implementing the strategy had no impact on student learning without a means of ruling out the possibility that the multiple-choice exam format assesses recall of memorized information rather than learning. The basic recall of information is low on Bloom's taxonomy, whereas the analysis of experimental data tested in the problems is a higher-level objective. The concept mapping strategy itself asks students to organize and synthesize, one of the highest levels (Anderson and Krathwohl, 2001). It is interesting to note that whereas performance on multiple-choice exams remains virtually constant, there was a marked increase in problem-solving ability. Problem solving may be a more adequate indicator of learning because the level of skill it assesses is closer to that helped by the CMAPs.

It is also possible that the increased performance in the test given to second-year students in 2007 (Table 3) results from increased retention of information from their CMAP constructions during the previous year. It would be interesting to evaluate transfer of knowledge from one course to another to determine whether this might have improved significantly. Although not directly assessed in the present experiment, it might be possible to envisage cross-course question exchanges. For example, a cell biology exam question could be given in the context of an exam in another course, after reformatting the question to correspond to material given in the other course. This would require collaboration between several instructors but could prove an interesting experiment in its own right.

Supplementary Material

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