Abstract
Introduction:
Farmers in flood- and cyclone-prone regions of India face recurrent shocks from extreme climatic events, yet their adaptive capacity remains constrained by limited access to timely and context-specific knowledge. Digital climate education offers promising opportunities to strengthen rural resilience; however, evidence on pedagogy-driven e-learning interventions for farmers is limited. This study aimed to develop, validate, and evaluate an e-learning module to enhance farmers’ climate resilience in vulnerable regions of Assam and Odisha.
Methods:
An e-learning module was developed using the ADDIE instructional design model and validated by 31 experts using structured rating scales. A 21-item knowledge test was constructed and refined through relevancy screening, pilot testing with 30 farmers, and item analysis (difficulty index, discrimination index, and point-biserial correlation), resulting in a final 10-item test. The effectiveness of the module was assessed using a pre-test/post-test design with 78 farmers, and data were analyzed using descriptive statistics, paired tests, and learning gain measures.
Results:
The module received high expert ratings across content, format, presentation, and usefulness. The final knowledge test demonstrated strong reliability (Cronbach’s α = 0.857). Significant improvements in knowledge scores were observed across both states (p < 0.001), with substantial mean gains (overall = 3.85) and large effect sizes. Normalized learning gains ranged from 0.599 to 0.672, indicating strong learning outcomes following exposure to the module.
Discussion:
The findings demonstrate that a systematically designed, multimedia-based e-learning module can effectively enhance farmers’ climate-related knowledge in vulnerable regions. The study highlights the potential of scalable digital extension approaches to complement traditional advisory systems and strengthen climate resilience in agriculture.
1 Introduction
Climate change poses one of the most pressing challenges to global food and livelihood security, disproportionately affecting smallholder farmers in developing countries (IPCC, 2022; Wheeler and von Braun, 2013). Increasing temperatures, erratic rainfall and rising incidences of floods, droughts and cyclones have intensified production risks and undermined the adaptive capacity of vulnerable farming communities (Porter et al., 2014; Morton, 2007). Strengthening farmers’ capacity to understand, anticipate and respond to climate-related risks has therefore become a critical priority for sustainable agricultural development. In this context, the present study aims to develop, validate and evaluate a digital e-learning module designed to enhance farmers’ climate resilience in climatically vulnerable regions of Assam and Odisha, India.
India is among the world’s most climate-sensitive regions, where nearly two-thirds of the population depends directly on climate-exposed agricultural systems (Aryal et al., 2020). Multiple assessments indicate that climate-related hazards are increasing in frequency and severity, threatening to reverse decades of development gains (World Bank, 2021). Farmers in eastern and northeastern India- particularly in Odisha and Assam- face recurrent climatic disruptions such as extreme floods, coastal cyclones and prolonged dry spells that lead to repeated crop loss, soil degradation and income instability (Department of Science and Technology, Government of India, 2019; Das et al., 2020; Bordoloi and Dutta, 2025; Saikia and Mahanta, 2025). These events erode household resilience and amplify existing socio-economic vulnerabilities (Dhanya and Ramachandran, 2015). While adaptation strategies such as crop diversification, weather advisory use, soil conservation and stress-resistant varieties are well documented (Altieri and Nicholls, 2017; Harvey et al., 2014), their adoption remains constrained by knowledge gaps, inadequate extension outreach and information asymmetries (Glendenning et al., 2010; Mittal and Mehar, 2016). Traditional extension systems often face limitations in effectively reaching dispersed rural populations, particularly in high-risk and remote areas (Anderson and Feder, 2007; Davis and Sulaiman, 2014).
Digital learning and ICT-enabled extension services have emerged as promising approaches to address these information gaps and support climate-resilient agriculture. Evidence suggests that digital platforms can enhance farmers’ access to timely information, improve preparedness for climatic risks and support adaptive decision-making (Aker, 2011; Fabregas et al., 2019). E-learning tools, in particular, offer structured, self-paced and interactive learning environments that can improve knowledge acquisition and skill development (Van Campenhout et al., 2021; Spielman et al., 2021). Multimedia-based agricultural training approaches have demonstrated effectiveness in improving comprehension among low-literacy learners (Zossou et al., 2009) and have outperformed traditional methods when tailored to local contexts (Kansiime et al., 2019). However, despite the growing emphasis on digital extension, systematic evidence on e-learning interventions specifically designed to build farmers’ climate resilience remains limited. Existing studies have largely focused on mobile-based advisories (Fafchamps and Minten, 2012), video-mediated extension (Van Mele et al., 2010; Coggins et al., 2025), or digital market information systems (Aker and Fafchamps, 2015), with relatively little attention to structured, pedagogy-driven e-learning modules that integrate multimedia content, contextual relevance and instructional design principles.
Instructional design frameworks such as ADDIE (Analysis, Design, Development, Implementation and Evaluation) have been widely applied in formal education (Heggart and Dickson-Deane, 2022; Md Khambari, 2019; Yeh and Tseng, 2019; Zhang et al., 2018) corporate training (Crompton et al., 2024; Cotter et al., 2023; Patel et al., 2018) and digital learning environments (Abuhassna and Alnawajha, 2023; Spatioti et al., 2022; Shakeel et al., 2023) to ensure systematic development of instructional content (Branch, 2009; Clark and Mayer, 2016). Empirical evidence suggests that ADDIE-based interventions improve learning outcomes, engagement and instructional effectiveness across diverse domains (Khuong et al., 2026; Tu et al., 2021; Shakeel et al., 2023; Tsagaris and Chatzikyrkou, 2026). However, its application in agricultural extension–particularly for climate change adaptation and resilience-building among farmers- remains limited, especially in developing country contexts. In India, there is a notable lack of empirically validated studies that apply structured instructional design frameworks to develop and assess digital learning tools for farmers. This gap is particularly critical because farmers’ ability to cope with and adapt to climate risks depends heavily on the quality, clarity and accessibility of information they receive (Mertz et al., 2009; John et al., 2013). Strengthening climate literacy and resilience-related knowledge is therefore fundamental for fostering sustainable agricultural development (FAO, 2015; Oborn et al., 2017). The suitability of the ADDIE framework for farmer-oriented digital learning lies in its iterative, learner-centered and context-sensitive approach. The model emphasizes needs assessment, content localization, structured delivery and continuous evaluation, which are particularly important in heterogeneous rural settings characterized by varying literacy levels, resource access and exposure to digital technologies. By integrating multimedia elements and self-paced learning structures, ADDIE aligns well with principles of adult learning and experiential knowledge acquisition (Knowles et al., 2014).
In view of these gaps, the present study contributes to the literature by applying a structured instructional design framework to develop and empirically evaluate a context-specific e-learning module aimed at enhancing farmers’ climate resilience. The study not only validates the instructional quality of the module but also assesses its effectiveness in improving farmers’ knowledge. The findings hold important implications for strengthening digital extension systems, designing scalable climate education interventions and informing policies aimed at promoting climate-resilient agriculture, particularly in hazard-prone regions.
2 Conceptual framework
The conceptual framework (Figure 1) illustrates the systematic process adopted to design, validate and evaluate an e-learning module aimed at strengthening farmers’ climate resilience in vulnerable regions of Assam and Odisha. The framework progresses through five interconnected stages. It begins by identifying the contextual antecedents- high climatic vulnerability and farmers’ constrained adaptive capacity- which establish the need for a scalable pedagogical intervention. Guided by this need, the second stage outlines the structured development of the e-learning module using the ADDIE instructional design model, ensuring context-specific and multimedia-based content. The third stage details the quality assurance process through expert validation of the module and psychometric testing of the knowledge assessment tool. The fourth stage represents implementation, where the intervention is delivered using a pre-test/post-test design among 78 farmers to evaluate learning gains. The final stage presents the outcomes, demonstrating enhanced cognitive gains and highlighting the broader implications for strengthening farmers’ adaptive capacity and supporting scalable digital extension models.
Figure 1
3 Materials and methods
3.1 Study area
The study was conducted in climatically vulnerable regions of Assam and Odisha, two of India’s most climate-sensitive states (Department of Science and Technology, Government of India, 2019). Assam, with a vulnerability index score of 0.620, ranks fifth nationally (Department of Science and Technology, Government of India, 2019) due to its geomorphology, recurrent flooding, and socio-economic fragility. Multiple assessments indicate that a large proportion of India’s most climate-vulnerable districts are concentrated in Assam (Mohanty and Wadhawan, 2021). State-level climate projections highlight increased risks of droughts and floods in the coming decades (Department of Environment and Forest, Government of Assam, 2015), with major flood events-such as those in 2022 and 2025- affecting millions of people. Odisha is similarly exposed to multiple climatic hazards and ranks third on India’s climate vulnerability index with a score of 0.633 (Department of Science and Technology, Government of India, 2019). The state has experienced an increasing frequency of severe cyclonic storms including Aila, Phailin, Fani, Amphan and Yaas over the past decade, underscoring its heightened disaster risk. Such extreme events disrupt agricultural livelihoods, making farmers highly vulnerable to climate-induced losses.
The districts of Lakhimpur and Dhemaji in Assam, and Puri and Khordha in Odisha were purposively selected due to their pronounced exposure to climate-related hazards (Council on Energy, Environment and Water (CEEW), 2021; Mathuria et al., 2022). These districts were chosen because farmers in these areas experience recurrent climate-related stresses, emphasizing the need for targeted capacity-building interventions. This necessitated the development of an e-learning module aimed at enhancing farmers’ resilience to climate change and related disasters. Location of the study area is presented in Figure 2.
Figure 2
3.2 Development of the e-learning module
The e-learning module was developed using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation), a widely accepted instructional design framework recommended by FAO (2021) and presented in Figure 3. The model provided a structured and iterative process to ensure pedagogical alignment and contextual relevance. The Analysis phase involved identifying farmers’ knowledge gaps relating to climate risks, adaptation practices and resilience-building strategies, and establishing the learning objectives and target audience profile. During the Design phase, the overall instructional strategy, sequencing and module structure were planned, with content localized to suit the agro-ecological context of the study areas and enriched using multimedia elements such as photographs, illustrations, charts, infographics and short video clips to enhance engagement. The Development phase entailed creating the module using the free, open-source authoring tool eXeLearning 2.8, which ensured replicability for institutions lacking proprietary software, and optimizing it for low-bandwidth settings common in rural locations. In the Implementation phase, the module was deployed among farmer groups through mobile-friendly platforms and offline-accessible formats, with necessary support provided for digital navigation. Finally, the Evaluation phase included structured expert review and assessment of learner performance to determine the usability and overall effectiveness of the module in enhancing farmers’ climate resilience capacity. The structure and visual interface of the developed e-learning module are illustrated in Figure 4.
Figure 3
Figure 4
3.3 Expert validation of the e-learning module
The initial draft of the module was reviewed by scientists from the Division of Agricultural Extension of Indian Agricultural Research Institute, New Delhi, who provided qualitative feedback for refinement. The revised version was subsequently validated by a panel of 31 experts, comprising 17 subject matter specialists from Krishi Vigyan Kendras (KVKs), which are district-level agricultural extension centres under the Indian Council of Agricultural Research (ICAR), along with 6 scientists from ICAR institutes and 8 officials from state line departments. The experts rated the module on four indicators: (i) content, (ii) format and language, (iii) presentation, and (iv) usefulness. A five-point rating scale was used for all items. Their responses were quantitatively analyzed using descriptive statistics (mean and standard deviation) to evaluate the perceived quality and adequacy of the module.
3.4 Development of the knowledge test
3.4.1 Item generation and screening
An initial pool of 21 items was generated based on the content of the e-learning module, covering topics such as farm-level vulnerability, causes of climatic risks, adaptation practices and resilience-building measures. These items were evaluated for content relevance by the same panel of 31 experts. Each item was rated as most relevant, relevant, undecided, or not relevant.
A relevancy weight was calculated for each item:
Items with a relevancy weight ≥ 0.80 were retained. This screening resulted in the selection of 15 items for pilot testing.
3.4.2 Pilot testing and item analysis
The preliminary 15-item test (Appendix Table B.1) was administered to 30 farmers from non-sample villages to conduct item analysis. Item difficulty, discrimination and point-biserial correlation were computed to determine the psychometric quality of each item.
3.4.2.1 Difficulty index
Item difficulty was calculated as the proportion of respondents answering an item correctly:where: = difficulty index of the item
= number of respondents answering correctly
= total number of respondents (30).
Values between 0.30 and 0.80 were considered acceptable.
3.4.2.2 Discrimination index
The discrimination index was calculated to assess the ability of each item to differentiate between high-performing and low-performing respondents. The total scores of all 30 pilot respondents were ranked from highest to lowest and then divided into three equal groups. Only the upper one-third (U) and lower one-third (L) groups- each consisting of g = N/3 = 10 respondents- were used for the computation.
The discrimination index was calculated using the formula:
where:
= number of correct responses among the upper one-third group
= number of correct responses among the lower one-third group
= size of each extreme group (here, 10)
This method is widely used in extension and educational research and yields values ranging from −1.00 to +1.00. Items with a discrimination index ≥ 0.30 were considered acceptable and retained for the final knowledge test.
3.4.2.3 Point-biserial correlation coefficient
To assess how strongly each item correlated with the overall test score, the point-biserial correlation coefficient was computed using:where:
= mean total score of respondents answering correctly.
= mean total score of respondents answering incorrectly.
= proportion of respondents answering correctly.
= proportion answering incorrectly.
= standard deviation of total scores.
Items with statistically significant values at the 5% or 1% level were considered valid.
3.4.3 Final item selection
Based on the combined criteria- difficulty index (0.30–0.80), discrimination index (≥0.30), and significant point-biserial correlation-10 items (I1, I3, I4, I5, I6, I9, I10, I11, I12, and I15) were retained in the final knowledge test.
3.5 Assessment of module effectiveness
To evaluate the effectiveness of the e-learning module, a pre-test/post-test design was employed. The respondents in this study were farmers residing in the selected climate-vulnerable districts of Lakhimpur and Dhemaji in Assam, and Puri and Khordha in Odisha. Farmers who were actively engaged in agricultural activities and willing to undergo the e-learning module were included in the study. Respondents were selected using purposive sampling to ensure relevance to climate risk exposure and the study objectives. A total of 78 farmers participated in the study (51 from Odisha and 27 from Assam). The sample size was determined based on feasibility considerations and accessibility of farmer groups. As the objective of the study was to assess learning effectiveness rather than to derive population-level estimates, the sample is not intended to be statistically representative of the entire farming population. The validated 10-item knowledge test was administered to all participants before and after exposure to the module. Descriptive statistics (mean, standard deviation and percentages) were used to summarize the knowledge scores, and the Shapiro–Wilk test was applied to assess data normality. Differences between pre-test and post-test scores were examined using paired t-tests and Wilcoxon signed-rank tests, and learning gains were computed using both absolute and normalized gain measures.
All data analyses were performed using R software (version 4.3.2), a free and open-source statistical computing environment.
4 Results
4.1 Expert evaluation of the developed e-learning module
Expert evaluation results indicate that the e-learning module received consistently high ratings across all assessed dimensions (Table 1). Mean scores ranged from 4.65 to 5.00, reflecting strong overall endorsement of the module’s instructional quality. The low standard deviation values (all below 0.50) suggest a high level of agreement among evaluators. Dimension-wise analysis further confirms this positive assessment, with all indicators recording mean scores above 4.80. Among them, presentation received the highest rating, followed by format and language, usefulness and content. Overall, the findings demonstrate that the module is well-structured, clear, relevant and effective in delivering climate resilience-related knowledge to farmers.
Table 1
| Aspect of the e-learning module | Item | Mean | Standard deviation |
|---|---|---|---|
| Content | C1_The instructional content is presented with clarity and comprehensibility. | 4.65 | 0.49 |
| C2_The content corresponds accurately with the defined learning objectives. | 5.00 | 0.00 | |
| C3_Each subject area is addressed comprehensively within the lesson. | 4.74 | 0.44 | |
| C4_The topics are reinforced with illustrative examples, and the practice tasks are appropriate for the students’ level. | 4.81 | 0.40 | |
| C5_The module allocates balanced attention and weight to all topics. | 4.94 | 0.25 | |
| Overall | 4.83 | 0.13 | |
| Format and language | F1_The structural layout is organized effectively to enhance learner engagement. | 4.94 | 0.25 |
| F2_The language used is simple and easy to understand. | 4.77 | 0.43 | |
| F3_The language is clear, concise and motivating. | 5.00 | 0.00 | |
| F4_The symbols used are clearly defined. | 5.00 | 0.00 | |
| F5_Navigational instructions within the module are concise and user-friendly. | 4.74 | 0.44 | |
| Overall | 4.89 | 0.15 | |
| Usefulness | U1_The e-learning module will encourage farmers to address climate change and natural disasters. | 4.81 | 0.40 |
| U2_The module facilitates self-directed learning at an individual pace. | 4.94 | 0.25 | |
| U3_The module will enable farmers to use their time more effectively. | 4.81 | 0.40 | |
| U4_The module will enhance farmers’ analytical thinking and reasoning skills related to climate change and natural disasters. | 5.00 | 0.00 | |
| U5_The module will act as supplementary material that meets the needs of farmers. | 4.81 | 0.40 | |
| Overall | 4.87 | 0.24 | |
| Presentation | P1_The subject matter follows a logical and progressive sequence. | 4.94 | 0.25 |
| P2_The instructional delivery is presented in a distinctive and original way. | 4.81 | 0.40 | |
| P3_The learning activities are presented clearly. | 5.00 | 0.00 | |
| P4_The presentation style is visually appealing and engaging for farmers. | 5.00 | 0.00 | |
| P5_An adequate number of examples illustrates each topic to ensure comprehension. | 4.94 | 0.25 | |
| Overall | 4.94 | 0.12 | |
| Overall | Content | 4.83 | 0.13 |
| Format and Language | 4.89 | 0.15 | |
| Usefulness | 4.87 | 0.24 | |
| Presentation | 4.94 | 0.12 |
Expert evaluation of the e-learning module across content, format and language, usefulness, and presentation dimensions (n = 31).
4.1.1 Differences across expert groups
Expert ratings were compared across the three expert groups- KVK subject matter specialists, ICAR scientists, and line department officials- using the Kruskal–Wallis H test (Table 2). No statistically significant differences were found for Content (H = 0.782, p = 0.6763), Format and Language (H = 0.268, p = 0.8745), Usefulness (H = 4.942, p = 0.0845), or Presentation (H = 1.159, p = 0.5601). These results indicate that the module was rated similarly across KVK, ICAR and line department experts, indicating broad acceptability across institutional categories.
Table 2
| Indicator | H | p-value |
|---|---|---|
| Content | 0.782 | 0.6763 |
| Format and language | 0.268 | 0.8745 |
| Usefulness | 4.942 | 0.0845 |
| Presentation | 1.159 | 0.5601 |
Comparison of expert ratings across institutional groups using the Kruskal–Wallis test.
4.2 Development and validation of the knowledge test
4.2.1 Difficulty index, discrimination index, and point-biserial correlation
A pilot test was conducted with 30 farmers (respondents) drawn from non-sample villages outside the selected study districts to evaluate the psychometric quality of 15 objective items developed to measure farmers’ knowledge of the content covered in the e-learning module. Difficulty indices, discrimination indices and point-biserial correlation coefficients were computed for each item. The results are presented in Table 3. The difficulty index values ranged from 0.033 to 0.967. Ten items (I1, I3, I4, I5, I6, I9, I10, I11, I12 and I15) met the criterion of an acceptable difficulty level (0.30 ≤ p ≤ 0.80). The remaining five items, I2 (p = 0.967), I7 (p = 0.033), I8 (p = 0.533), I13 (p = 0.367) and I14 (p = 0.433), either fell outside this range or did not meet the other criteria required for retention. Discrimination indices ranged from −0.10 to 0.90. Ten items achieved discrimination values of 0.50 or above, satisfying the threshold of DI ≥ 0.30. Items I2 (DI = −0.10), I7 (DI = 0.10), I8 (DI = 0.30), I13 (DI = 0.20) and I14 (DI = 0.10) did not meet the criterion. The point-biserial correlation coefficients (r_pb) for the 10 selected items ranged from 0.397 to 0.771, all statistically significant at the 5% level (p < 0.05) and 1% level (p < 0.01). The five rejected items exhibited either non-significant or negative point-biserial correlations, indicating weak association with the total test score. To visually represent the psychometric quality of the pilot items, a difficulty-discrimination plot was constructed (Figure 5). The figure displays the distribution of all 15 items along the two key validation parameters. The shaded rectangular region indicates the acceptable range for item retention, defined as a difficulty index between 0.30 and 0.80 and a discrimination index of at least 0.30. As illustrated, 10 items fell within the acceptable region and were retained for the final knowledge test, while the remaining five items fell outside the optimal range and were therefore excluded.
Table 3
| Items | Difficulty index | Discriminatory index | Point Bi-serial correlation | Significance level | Selected (S)/rejected (R) |
|---|---|---|---|---|---|
| What farming practice involves planting a variety of crops in a single field to reduce the impact of crop failure? (I1) | 0.667 | 0.900 | 0.767 | p < 0.01 | S |
| Which of the following is NOT a climate change-induced natural disaster faced by Indian farmers? (I3) | 0.633 | 0.600 | 0.585 | p < 0.01 | S |
| Which type of soil management helps farmers retain moisture and prevent soil erosion? (I4) | 0.533 | 0.500 | 0.429 | p < 0.05 | S |
| What farming technique involves constructing raised beds to prevent waterlogging? (I5) | 0.567 | 0.800 | 0.656 | p < 0.01 | S |
| Which government agency in India provides early warnings related to climate disasters? (I6) | 0.433 | 0.500 | 0.430 | p < 0.05 | S |
| What is the primary purpose of constructing windbreaks and shelterbelts? (I9) | 0.567 | 0.700 | 0.556 | p < 0.01 | S |
| Which scheme provides crop insurance to Indian farmers? (I10) | 0.567 | 0.800 | 0.606 | p < 0.01 | S |
| Which practice is NOT a part of Integrated Pest Management (IPM)? (I11) | 0.467 | 0.600 | 0.450 | p < 0.05 | S |
| How does agroforestry benefit farmers during natural disasters? (I12) | 0.567 | 0.700 | 0.508 | p < 0.01 | S |
| Which traditional technique involves constructing earthen embankments to retain water? (I15) | 0.600 | 0.700 | 0.399 | p < 0.05 | S |
| What term refers to a farmer’s ability to adapt and recover from climate-induced disasters? (I2) | 0.967 | −0.100 | −0.280 | Not significant | R |
| Which drought-resistant crop is commonly cultivated in India? (I7) | 0.033 | 0.100 | 0.177 | Not significant | R |
| What farming practice involves collecting and storing rainwater? (I8) | 0.533 | 0.300 | −0.008 | Not significant | R |
| Which crop is heat-tolerant and suitable for rising temperatures? (I13) | 0.367 | 0.200 | 0.081 | Not significant | R |
| What social initiative aims to disseminate climate information at the community level? (I14) | 0.433 | 0.100 | −0.191 | Not significant | R |
Item difficulty, discriminatory index, and point bi-serial correlation values for the knowledge test items.
Figure 5
4.2.2 Reliability of the final knowledge test
The internal consistency reliability of the final 10-item knowledge test was estimated using Cronbach’s alpha and split-half reliability analysis (Table 4). Cronbach’s alpha for the final scale was α = 0.8568, indicating high internal consistency. The split-half correlation was 0.7479, and the Spearman–Brown corrected reliability coefficient was 0.8557, further confirming the reliability of the instrument.
Table 4
| Statistic | Value |
|---|---|
| Cronbach’s alpha | 0.857 |
| Split-half correlation r | 0.748 |
| Spearman–Brown reliability | 0.856 |
Reliability of the final 10-item knowledge test.
4.3 Effectiveness of the developed e-learning module
4.3.1 Pre-test and post-test knowledge scores
The descriptive statistics for pre-test and post-test scores are presented in Table 5. Among participants from Odisha, the mean pre-test score was 4.33 (SD = 1.47), while the mean post-test score increased to 8.35 (SD = 1.02). The mean knowledge gain was 4.02 points (SD = 1.66). For participants from Assam, the mean pre-test score was 4.26 (SD = 1.26), and the mean post-test score was 7.78 (SD = 1.37), resulting in a mean gain of 3.52 points (SD = 1.89). Across the full sample, the mean pre-test score was 4.31 (SD = 1.39), increasing to 8.15 (SD = 1.17) in the post-test, with an overall mean gain of 3.85 points (SD = 1.74). To visualize the magnitude of improvement, Figure 6 presents the pre-test and post-test mean knowledge scores for farmers across Odisha, Assam and overall.
Table 5
| State | n | Pre_mean | Pre_SD | Post_mean | Post_SD | Mean_diff | SD_diff |
|---|---|---|---|---|---|---|---|
| Odisha | 51 | 4.33 | 1.47 | 8.35 | 1.02 | 4.02 | 1.66 |
| Assam | 27 | 4.26 | 1.26 | 7.78 | 1.37 | 3.52 | 1.89 |
| Total | 78 | 4.31 | 1.39 | 8.15 | 1.17 | 3.85 | 1.74 |
Comparison of pre-test and post-test knowledge scores of farmers in Odisha and Assam.
Figure 6
4.3.2 Tests of normality
The Shapiro–Wilk test was conducted to examine the normality of the pre-test and post-test distributions (Appendix Table A.1). The pre-test and post-test scores for participants in Odisha showed statistically significant deviations from normality (W = 0.9316, p = 0.0057; W = 0.9052, p = 0.0006, respectively). The total sample also exhibited non-normal distributions for both pre-test (W = 0.9329, p = 0.0005) and post-test scores (W = 0.9216, p = 0.0001). For participants from Assam, pre-test (W = 0.9376, p = 0.1062) and post-test scores (W = 0.9243, p = 0.0501) were close to or within acceptable limits of normality. Given these patterns, both parametric and non-parametric paired tests were conducted.
4.3.3 Paired tests, effect sizes, and learning gains
Paired-samples t-tests and Wilcoxon signed-rank tests were used to analyse the differences between pre-test and post-test scores (Table 6). For Odisha, the paired t-test yielded t = 17.343 (p < 0.001), and the Wilcoxon test produced W = 1.5 (p < 0.001). The mean gain of 4.02 points corresponded to a Cohen’s d value of 2.429. For participants from Assam, the paired t-test yielded t = 9.680 (p < 0.001), and the Wilcoxon test returned W = 0.0 (p < 0.001), with a mean gain of 3.52 points and Cohen’s d of 1.863. Across the full sample, the paired t-test indicated t = 19.479 (p < 0.001), and the Wilcoxon test resulted in W = 2.5 (p < 0.001). The mean gain of 3.85 points corresponded to an effect size of d = 2.206. Normalized learning gains were also computed. Mean normalized gain values were g = 0.672 for Odisha, g = 0.599 for Assam, and g = 0.647 for the full sample. Percentage gains relative to the maximum possible score were 40.20, 35.19, and 38.46%, respectively.
Table 6
| Group | t-stat | t p-value | Wilcoxon W | Wilcoxon p | Mean_diff | Cohen’s d | Mean norm. Gain | % gain |
|---|---|---|---|---|---|---|---|---|
| Odisha | 17.34 | <0.001 | 1.5 | <0.001 | 4.02 | 2.43 | 0.672 | 40.20 |
| Assam | 9.68 | <0.001 | 0.0 | <0.001 | 3.52 | 1.86 | 0.599 | 35.19 |
| Total | 19.48 | <0.001 | 2.5 | <0.001 | 3.85 | 2.21 | 0.647 | 38.46 |
Results of paired tests, effect sizes, and normalized learning gains.
5 Discussion
The present study sought to validate and assess the effectiveness of an e-learning module designed for farmers in Odisha and Assam, with a focus on enhancing their knowledge of improved agricultural practices and resilience building. The results demonstrate strong expert endorsement of the module, high psychometric quality of the knowledge test and substantial learning gains among farmers. Experts across KVKs, ICAR institutes and line departments provided uniformly high ratings for the content, format, language, usefulness and presentation of the e-learning module. High expert consensus on instructional materials is consistent with earlier studies that highlight the importance of expert-validated digital learning resources in agricultural extension (Aker, 2011; Davis and Sulaiman, 2014; Saravanan and Suchiradipta, 2017). The strong positive ratings observed for content quality align with previous work showing that contextualized and locally relevant information enhances acceptability of digital tools among practitioners (Van Campenhout, 2021; Fabregas et al., 2019). The high ratings for clarity and understandability of language support findings by Mittal and Mehar (2016), who emphasized that simplified language increases the usability of ICT-based advisory systems.
Experts also rated presentation and multimedia structure of the module very highly. This corresponds with Mayer’s Cognitive Theory of Multimedia Learning (Mayer, 2005), which posits that coherent structure and multimedia alignment significantly improve learner engagement. Studies in digital extension similarly highlight the role of structured visual content in promoting comprehension among farmers with diverse literacy levels (Zossou et al., 2009; Coggins et al., 2022). The absence of significant differences in ratings across institutional categories suggests that perceptions of module quality were broadly shared, which is consistent with earlier research showing that well-designed ICT tools achieve cross-institutional relevance (Qiang et al., 2012; Spielman et al., 2021).
The pilot analysis of the 15 knowledge items showed that 10 items met all psychometric criteria- difficulty index (0.30–0.80), discrimination index (>0.30), and significant point-biserial correlation. These findings are similar to earlier studies validating agricultural knowledge tests, such as those by Jena et al. (2019), Latha et al. (2022), Ptel et al. (2022), and Bellagi et al. (2022), which reported similar ranges of difficulty and discrimination indices during item refinement. The final retained items exhibited strong discriminating ability (DI: 0.50–0.90), consistent with psychometric studies emphasizing that items with higher discrimination indices better differentiate between high- and low-knowledge learners (Ebel and Frisbie, 1991; Crocker and Algina, 2008). The reliability analysis showed high internal consistency (Cronbach’s α = 0.857; Spearman–Brown = 0.856), aligning with recommended thresholds for educational and extension research tools (Nunnally and Bernstein, 1994). Comparable reliability levels were reported in studies developing ICT-based agricultural learning assessments in India, Bangladesh and Africa (Kabir et al., 2022; Nkandu and Phiri, 2022; Errabo et al., 2024; Pal et al., 2024). The significant item-total correlations further confirm construct validity, echoing patterns observed in earlier learning assessments (Sarkar et al., 2014; Mallah et al., 2020; Latha et al., 2022; Han and Abdul Rahman, 2025). The rejected items showed poor statistical properties, with extreme difficulty (I2, I7), low discrimination (I8, I13, I14), or non-significant item-total correlations. Similar issues were encountered in test development studies by Sarkar et al. (2014), Kumari et al. (2020) and Sinha et al. (2020), who noted that overly easy or overly difficult items typically fail to discriminate effectively. Collectively, these results indicate the robustness of the final 10-item knowledge test for evaluating learning outcomes associated with the e-learning module.
The pre-test and post-test analysis demonstrated substantial knowledge gains among farmers. The improvement of 3.85 points (out of 10) across the full sample is consistent with earlier ICT-mediated training studies reporting large effect sizes (Aker and Ksoll, 2015; Shivaraju et al., 2017; Malini et al., 2020; Gupta et al., 2024). State-wise analysis showed slightly larger gains in Odisha (mean gain = 4.02) compared to Assam (3.52), aligning with variations in baseline exposure to agricultural advisories reported in studies comparing eastern Indian states (Glendenning et al., 2010; Paul et al., 2024; Sahu et al., 2024). The higher initial variation in Odisha may have contributed to greater measurable improvement, a phenomenon noted in adult learning contexts (Lwoga, 2011; Lwoga et al., 2011; Knowles et al., 2014). Given the non-normal distribution of several score variables, the robustness of results was ensured through both parametric and non-parametric testing. Similar statistical approaches have been used in evaluating farmer learning from ICT extensions in Nigeria (Aker et al., 2016), Kenya (Baumüller, 2018), and Bangladesh (Rahman et al., 2024), strengthening methodological reliability.
The substantial learning gains observed among farmers can be attributed to several reinforcing factors emerging from both the study design and the broader literature on adult learning and ICT-enabled knowledge dissemination. One key factor is the learner-centered and multimedia-based design of the module, which aligns with Mayer’s (2005) Cognitive Theory of Multimedia Learning and the broader empirical evidence showing that multimedia instruction significantly enhances comprehension, cognitive processing and retention among adult learners (Clark and Mayer, 2016). Research in agricultural extension similarly demonstrates that video-mediated instruction, interactive visuals and modular learning structures improve farmers’ understanding of complex agronomic concepts (Zossou et al., 2009; Van Campenhout et al., 2021). Additionally, the relevance of the module’s content to the local farming context likely contributed to high engagement and strong learning outcomes, echoing findings from Lwoga et al. (2011), Fabregas et al. (2019) and Kansiime et al. (2019), who argued that contextualized digital content significantly enhances learner uptake. The self-paced nature of the module may have further amplified learning gains, as adult learning theory emphasizes autonomy and self-direction as critical drivers of effective knowledge acquisition (Knowles et al., 2014). This is supported by empirical studies from sub-Saharan Africa and Asia indicating that self-paced e-learning systems allow farmers to revisit content and learn at a comfortable speed, thereby improving comprehension (Davis and Sulaiman, 2014; Lwoga and Komba, 2015; Xu et al., 2023; Shravani et al., 2025). Another contributing factor is the clarity and simplicity of language used in the module, which is consistent with research showing that linguistic accessibility plays a crucial role in the effectiveness of digital agricultural advisories (Mittal and Mehar, 2016; Tzachor et al., 2023). The structured sequencing and scaffolding embedded in the module may also have played a critical part, as instructional scaffolding and logical flow are associated with improved knowledge gain in ICT-based extension systems (Rogers and Twidle, 2013; Verschaffel et al., 2019; Spielman et al., 2021). Collectively, these elements likely interacted to produce the high normalized gains (0.60–0.67) and large effect sizes (Cohen’s d above 2) observed in this study.
5.1 Policy implications
The findings offer important insights for strengthening digital climate education and agricultural extension policy in India, particularly in flood- and cyclone-prone regions. The demonstrated effectiveness of the module indicates that structured e-learning tools can complement existing extension services and may be integrated into national and state-level climate adaptation initiatives, including National Innovations in Climate Resilient Agriculture (NICRA), Agricultural Technology Management Agency (ATMA), ICAR–NARES programs and disaster-risk reduction schemes. This aligns with long-standing policy arguments advocating modernization of public extension through digital innovations (Sulaiman and Hall, 2004; Rivera and Sulaiman, 2009). Given the widespread penetration of mobile phones in rural areas, deploying mobile-responsive climate learning content offers a promising pathway to reach hazard-exposed and geographically dispersed farmers, consistent with the potential highlighted by Aker (2011) and Qiang et al. (2012).
The results further emphasize the importance of building digital literacy and ICT competencies among frontline extension personnel, echoing concerns raised by Saravanan (2010), Heeks (2017) and Mukherjee et al. (2025). Embedding the module within training-of-trainers programmes for Krishi Vigyan Kendras (KVKs) and line departments could improve institutional delivery of climate-risk education and enhance long-term sustainability. The strong expert validation also suggests that the module can be adapted across institutional and regional contexts, supporting ongoing recommendations for harmonized digital extension frameworks (Davis and Sulaiman, 2014). Additionally, customizing modules for region-specific climate hazards and crop systems- an approach supported by Lwoga et al. (2011) and Van Campenhout et al. (2021)- could increase relevance and usability. Finally, a blended extension model that integrates digital climate learning with in-person advisory support may be especially effective, as evidence shows that hybrid approaches improve learning and adoption outcomes (Harder et al., 2016; Fielke et al., 2020).
5.2 Future research directions
The results open several avenues for future investigation. A major direction for future research is the assessment of long-term knowledge retention, as the current study measured only immediate post-intervention outcomes. Prior studies in agricultural e-learning and active learning environments (Freeman et al., 2014) have highlighted the need to understand whether short-term gains translate into sustained knowledge. Another promising direction involves examining behavioural and adoption outcomes. Since knowledge does not automatically translate into practice, future studies should explore whether farmers who benefit from digital modules subsequently adopt the recommended practices, as suggested by Van Campenhout et al. (2021). Additionally, evaluating the module’s impact on agricultural productivity, income improvements and sustainability indicators would contribute to broader developmental and policy debates aligned with FAO (2017) and Giller et al. (2021), who emphasise the role of knowledge systems in sustainable intensification.
Comparative studies assessing the effectiveness of e-learning relative to traditional or blended learning approaches may provide insights into optimal pedagogical combinations for rural contexts. Gender-disaggregated analyses also represent a critical research need, given well-documented gender disparities in digital access and ICT adoption in rural India (Bala and Singhal, 2018; Singh et al., 2025). Another future direction includes exploring the integration of artificial intelligence and personalized learning analytics to tailor e-learning content to farmers’ specific knowledge levels and preferences, echoing global trends noted by the World Bank (2025). Furthermore, examining the cost-effectiveness of digital training relative to conventional farmer field schools or group trainings would help inform public investment decisions, as prior evaluations of mobile-based interventions (Aker and Ksoll, 2015) underscore their potential for scaling at low marginal cost.
5.3 Limitations
Despite its contributions, the study has several limitations that must be acknowledged when interpreting the results. The evaluation focused on short-term changes in knowledge and did not capture long-term retention, behavioural change, or productivity impacts. This is a common limitation in digital learning studies, as noted in earlier research (Freeman et al., 2014). The study was geographically limited to two states- Odisha and Assam- which, although agriculturally diverse, may not fully represent the heterogeneity of farming contexts across India. Variations in agro-climatic conditions, literacy levels, market access and institutional support may influence the generalizability of the findings. The study also did not examine digital access barriers, which remain a persistent challenge in rural regions (Heeks, 2017; Mittal and Mehar, 2016). Limited internet connectivity, device availability and digital skills may affect the scalability of such modules. Finally, the knowledge test, while psychometrically strong, focussed on cognitive outcomes alone and did not incorporate attitudinal or skill-based dimensions, which are increasingly recognized as critical for holistic capacity building (Kraiger et al., 1993; Agi et al., 2018). These limitations offer important considerations for interpreting the findings and highlight the need for more comprehensive and longitudinal evaluations in future research.
6 Conclusion
The present study validated and evaluated an e-learning module aimed at enhancing farmers’ knowledge of improved agricultural practices in Odisha and Assam. The findings demonstrate that the module was highly rated by experts across institutions, with consistently strong scores for content accuracy, clarity of format and language, usefulness, and presentation. The psychometric evaluation of the knowledge test further confirmed its reliability and validity, with 10 items meeting all criteria for difficulty, discrimination and point-biserial correlation. These results indicate that the assessment tool effectively captured variations in farmers’ understanding of the module’s content. The pre-test and post-test evaluation revealed substantial and statistically significant gains in knowledge across both states. Large effect sizes, high normalized learning gains and consistent improvements across respondent groups affirm the effectiveness of the e-learning approach in enhancing cognitive outcomes. These findings contribute to the growing body of evidence supporting the use of ICT-based pedagogical tools in agricultural extension and underscore their potential to complement traditional advisory mechanisms. The study underscores the value of well-designed digital learning interventions that are context-specific, multimedia-driven and learner-centered. As agricultural systems evolve under the pressures of climate change, market integration and technological advancement, scalable digital learning solutions such as the one developed in this study can play an important role in strengthening farmers’ adaptive capacity. Integrating such modules into mainstream extension programs, local capacity-building initiatives and digital advisory platforms could enhance their reach and impact.
While the study provides encouraging evidence on the effectiveness of the module, it also highlights the need for continued research on long-term knowledge retention, behavioural adoption and economic benefits associated with digital learning. Addressing infrastructural and digital access constraints remains vital to ensuring inclusiveness and equity in technology-enabled extension. Future work may also focus on developing localized content, gender-responsive digital strategies and more immersive learning modalities to further enhance farmers’ learning experiences. Overall, the study demonstrates that thoughtfully designed e-learning modules can significantly enhance farmers’ knowledge and hold promise as a sustainable and scalable tool for agricultural capacity development.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Approval was granted by the Research Ethics Committee of ICAR–Indian Agricultural Research Institute (IARI), New Delhi, India (File No. Ag.Extn/2023/339; dated September 26, 2023). The study was conducted in accordance with the Declaration of Helsinki. Verbal informed consent was obtained from all participants prior to data collection. Participants were informed about the purpose of the study, the voluntary nature of participation, and their right to withdraw at any time without any consequences. No personally identifiable information was collected, ensuring full confidentiality and anonymity of the responses.
Author contributions
SKG: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft. RNP: Conceptualization, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft. RRB: Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing – review & editing. SA: Formal analysis, Methodology, Software, Validation, Visualization, Writing – review & editing. SR: Methodology, Visualization, Writing – review & editing. SS: Methodology, Writing – review & editing. BD: Software, Writing – review & editing. MY: Formal analysis, Software, Visualization, Writing – review & editing. AL: Methodology, Software, Writing – review & editing. SWQ: Investigation, Writing – review & editing. TGC: Writing – review & editing. SM: Writing – review & editing. BG: Writing – review & editing. BB: Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fclim.2026.1756972/full#supplementary-material
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Summary
Keywords
ADDIE model, climate education, climate resilience, digital agriculture, effectiveness, e-learning module, India, validation
Citation
Gorai SK, Padaria RN, Burman RR, Aiswarya S, Rakshit S, Sarkar S, Das B, Yeasin M, Lama A, Quader SW, Chiru TDG, Mukherjee S, Ghosh B and Barman B (2026) Digital climate education for rural resilience: validation and effectiveness of an e-learning module for farmers in flood- and cyclone-prone regions of India. Front. Clim. 8:1756972. doi: 10.3389/fclim.2026.1756972
Received
29 November 2025
Revised
29 March 2026
Accepted
13 April 2026
Published
12 May 2026
Volume
8 - 2026
Edited by
Patrick Ngulube, University of South Africa, South Africa
Reviewed by
Toshiko Kikkawa, Keio University, Japan
Shreesha Pandeya, Kentucky State University, United States
Updates
Copyright
© 2026 Gorai, Padaria, Burman, Aiswarya, Rakshit, Sarkar, Das, Yeasin, Lama, Quader, Chiru, Mukherjee, Ghosh and Barman.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Rabindra Nath Padaria, rabi64@gmail.com; S. Aiswarya, aishuambady@gmail.com; Shantanu Rakshit, rakshitshantanu90@gmail.com; Bhagirath Das, bhagirathdas5@gmail.com
†PRESENT ADDRESSBhaskar Ghosh, SEEM Division, CSB-Central Muga Eri Research and Training Institute, Lahdoigarh, Jorhat, Assam, India
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