Knowledge gain and training impact among fisheries trainees: A hierarchical layered knowledge analysis framework (HLKA)

Introduction

In the wake of emerging food and nutrition challenges, aquaculture is an imperative option for providing goodquality animal protein to undernourished people. According to the Food and Agriculture Organisation (FAO, 2024) [17] , 713 and 757 million people, corresponding to 8.9 and 9.4 per cent of the global population, respectively, may have faced hunger in 2023. Sustainable development goal 2 (SDG 2) is ensuring access to safe, nutritious and sufficient food for all people all year round and eradicating all forms of malnutrition by 2030. Fish and fishery products as a source of animal protein can be one remedy to the attainment of Sustainable Development Goal 2 agenda (zero hunger). According to hand handbook on fisheries statistics (2023), fisheries and aquaculture remain to be as an important source of food, nutrition and livelihood to 28 million fishers and fish farmers. The world fisheries sector depends on the culture and capture fisheries, however, due to overexploitation and habitat degradation fishery stocks are declining in the sea and oceans, aquaculture is the only option for fish production, but due to the limitation of inland fisheries resources, it is challenging to full fill the protein requirement for growing papulation at existing infrastructure. Improving the infrastructure according to modern farming technology and providing the training and skills to the farmers to exploit production technologies sustainably. Aquaculture production not only provides livelihood to the farmers but also reduces stress on capture fisheries. India is the 3rd largest aquaculture and fishproducing country, contributing 16 per cent of total inland and 5 per cent of marine production in the world fish production (Handbook on Fisheries Statistics, 2023) [23] . Total fish production has reached a time high of 175.45 lakh tonnes during 2022-23, comprising 44.32 lakh tonnes of marine output and 131.13 lakh tonnes of inland production (Handbook of Fisheries Statistics, 2023). In agriculture economic land, labour, capital and management are considered as factors of production (Parker et al, 1972) [45] . Labour is the most important factor that mobilizes production functions. Farmers' contribution to aquaculture is significant in transforming the raw materials (seed, feed, fertilisers etc.) into marketablefish. There are five types of capital: social, political, human, financial, and environmental capital (Etongo and Gill, 2022) [14] . Human capital consists of human resources and skills and knowledge associated with them which are important to enhance the workforce in aquaculture sectors for success and effectiveness, individuals and organizations must www.extensionjournal.com continuously gain knowledge to remain competitive (Sheikh, 2008) [51] . Imparting knowledge, abilities, skills, or attitudes (KASAs) to participants involves a process governed by certain strategies. Training is one of the strategies for imparting skills and knowledge to the trainees. In order to attract and empower rural youths in agriculture and allied sectors, Indian council of Agriculture Research (ICAR) launched a project named "Attracting and Retaining Youth in Agriculture" (ARYA), in 2015-16 to provide, business, sustainable income, employment opportunities and capacity development to the rural youth in agriculture and allied sectors. This can be achieved by implementing various skill development training programmes (Anonymous, 2020) [2] . Lynton and Pareek (2002), define training as the development and delivery of information that people will use after attending the training. Training is a process in which trainees develop information, skills, and attitudes through a programme sequence. It also fills the gap between what they have and what they require (Beach, 1980) [6] . Training is a structured communication process for workforce development that seeks to bring about desired changes in knowledge (K), skills (S), practise (P), attitude (A), behaviour (B), and other things (O) that correspond with a position's requirements (Misra, 1999) [37] . "Learning by doing" and "seeing and believing" are two important principles of extension education. Learning by doing is associated with the educational theory called "experiential education" which holds that learning happens using the transformation of experience (Kolb & Kolb, 2005) [29] . Behavioural change is a pre-requisite for technological adoption, which is mostly governed by attitude (Ndah et al., 2011; Wandji et al., 2012; Olatunji and Nwaogwugwu, 2013) [41,57,43] . The training programme is a strategy to change the attitude, boost confidence, and morale, and motivate them through experimental learning leading to better performance (Khairul and Kamariah, 2011) [27] . Training is related to direct financial costs, time of employees devoted to training, training planning, and time of managers (Kumar et al., 2024) [30] . Many researchers agreed in the 1990s that most investments in training were wasted (Ford and Weissbein, 1997) [18] . Therefore, training evaluation is the most key component of any training program. Evaluating the processes of the training programme helps to make subsequent changes in training and development programmes, i.e., to optimise processes and increase yields or outputs in every organisation (i.e., knowledge, skills, attitudes) (e.g.) (Ferris et al., 1999; Aragon-Sanchez et al.,2003; Park et al.,2003; Mollahoseini and Farjad, 2012; Chalupa, 2020) [16,3,44,39,7] . According to Harper and Bell (1982) [24] evaluation as planned collection, collation and analysis of information to make judgements about worth. Goldstein (1993) [19] outlines evaluation as the "systematic collection of descriptive and judgmental information necessary to make effective decisions related to selection, adoption, value and modification. [3,25] . According to Kirkpatrick (1996) [28] , training (Goldstein,1993; Mann,1996) [19,34] . Most of the training is evaluated through participants' reactions collected at the end of the course. However, only a small number of programs use methods like pre-and post-tests or assessments completed by the participants themselves (Phillips and Phillips, 2016) [47] . Furthermore [36] . Around 80% of farmers are small or marginal, with shrinking landholdings due to population growth (De & Singh, 2017) [12] . To provide technical support to fish farmers, the College of Fisheries, Lembucherra (CAU), Agartala, offers both on-campus (at college) and offcampus (at KVK and Village) training in areas like pond management, feed formulation, fish processing, entrepreneurship, and capacity building. Farmers from the northeastern region who participated in a different training programme conducted by the College of Fisheries Lembucherra, Agartala, were selected for the study.

Sample and procedures

Complete enumeration sampling technique was used in the present study where trainees from different states of the north east region participated in on and off-campus training programmes organised by the College of Fisheries, Tripura, Agartala (CAU). Total 140 farmers trainees from oncampus training programmes off-campus (KVK and village panchayat) were selected to evaluate the training www.extensionjournal.com programmes in terms of "knowledge and skill" gained and "effectiveness" of the training programmes.

Methodology

In the present study, a mixed methodological approach was used where qualitative and quantitative data were collected by using a semi-structured interview schedule Hierarchical Layered Knowledge Assessment (HLKA) approach: Hierarchical Layered Knowledge Assessment (HLKA) is the most versatile methodology for training evaluation, which can be tailored to the different kinds of agricultural and industrial training programmes. This methodology merges the structured framework of Bloom's taxonomy of learning. The taxonomy was a means to express the different kinds of intellectual skills and abilities (Wilson, 2016) [58] . It consists of six levels: (I) remembering, (II) understanding, (III) applying, (IV) analysing, (V) evaluating, and (VI) creating. It emphasises layered data analysis, contextual adaptation and applied knowledge to measure knowledge and skill gain to evaluate the training programmes. methodology has been broken down as follows: Layer 3: Applied knowledge: Applying involves using a method or process by performing or implementing it. It refers to situations where knowledge is put into practice, often through outputs such as models, presentations, interviews, or simulations.

Confidence level

Confidence involves a degree of belief that one relies on something. It was measured by using a 5-point Likert scale from high confidence [5] to low confidence [1] . Confidence judgments are widely acknowledged as important in evaluating knowledge (De Finetti, 1965; Good, 1979) [10,20] . Confidence can be understood as a type of probability prediction (Dawid, 1986) [9] , representing an estimate of how likely it is that a given answer is correct.

Post-test analysis

The collected data were analysed, and average score, overall score and confidence score were calculated by using statistical tools, and a knowledge growth curve was generated.

1) The following formula calculates the average score and overall score:

2) The relative knowledge gain is calculated using the formula: Bandura (1997) [5] Self-efficacy influences learning, motivation, behavioural change and adoption of practices. [49] Training improved self-confidence and adoption behaviour.

Conceptual framework of Hierarchical Layered Knowledge Assessment (HLKA):

Result and Discussion A) Socio-economic profile

The socio-economic profile of the trainees was assessed using variables such as age, gender, caste, educational qualification, family size, annual income, occupation, fisheries experience, pond area, innovativeness, market orientation, scientific orientation, economic orientation, mass media exposure, and extension contact. Observed from the figure 2, the socio-economic profile of the trainees revealed that most respondents were young (59.04%), female (60.11%), belonged to the Scheduled Tribe category (44.68%), and had education up to the high school level (39.36%). Similar findings were reported by Argade et al.

(2023) [4] , who observed that a majority of fish farmer trainees belonged to the younger age group. Most trainees had medium-sized families (57.98%), which is in line with the findings of Nirmalkar et al. (2022) [42] , and possessed pond areas up to 0.40 ha (64.89%), corroborating the results of Upadhyay et al. (2012) [56] . The majority of respondents had 1-5 years of fisheries experience (64.36%), supporting the observations of Lawal (2021) [31] , who reported that less experienced fish farmers are generally more receptive to new technologies and improved practices. Aquaculture was the primary occupation for 62.77 per cent of the trainees.

Most respondents belonged to the medium annual income category (81.91%) and exhibited medium levels of innovativeness (77.66%), market orientation (67.02%), scientific orientation (70.21%), and economic orientation (63.23%). Similar trends were reported by Dubey et al., 2011; Goswami, 2016) [13,22] , who found that fish farmers commonly exhibit moderate levels of adoptionrelated characteristics. Furthermore, the majority had medium levels of mass media exposure (58.51%) and extension contact (61.70%). Singh and Tanwar (2020) [52] emphasized that extension services, ICT tools, and social media platforms play a crucial role in disseminating information and enhancing technology adoption among fish farmers. These findings indicate that the trainees were predominantly small-scale fish farmers with moderate socio-economic status and considerable potential to benefit from training and extension intervene. 2) Among the statements, the highest proportion of correct responses was recorded for the question, "Which colour of water is good for fish growth?", where 71.43 per cent (100 respondents) answered correctly with an average confidence level of 3.51. This finding indicates that the farmers possessed relatively good knowledge regarding pond water quality management, which is a fundamental aspect of fish culture. In contrast, the lowest proportion of correct responses was observed for the question, "Saprolegniasis disease is mostly occur in which stage of fish?", where only 15.00 per cent (22 respondents) provided the correct answer.

Despite the low level of knowledge, the average confidence level for this question was relatively high (3.55), suggesting the presence of overconfidence among the respondents regarding fish disease management. Similar observations were reported by Argade et al. (2023) [4] , who found that fish farmers had varying levels of knowledge across different aquaculture practices prior to training and that skill development programmes significantly improved their knowledge levels. Similar knowledge gaps were observed in areas related to liming requirements (27.14%), fish seed stocking time (30.71%), and nursery pond management (33.57%). Misra et al. (2024) [38] , who reported that most participants exhibited medium to high knowledge levels after undergoing integrated fish-cum-poultry farming training, indicating that well-structured training programmes can significantly enhance farmers' understanding and adoption of scientific aquaculture practices. From the Table 2 and Figure 2 the overall average percentage of correct responses in the pre-test was 37.86 per cent, indicating a relatively low level of baseline knowledge among the fisheries farmers before attending the training programme.

The mean confidence level across all statements was 3.36 on a five-point scale, reflecting moderate confidence among respondents.

Layer 1: Recall and Recognition

A total of ten objective questions covering fundamental aspects of fish culture, including water quality management, fish species identification, feeding practices, and pond management, were administered to the trainees after completion of the training programme. The results presented in Table 3 indicate a substantial improvement in the recall and recognition ability of the trainees. Among the ten statements, the highest percentage of correct responses was recorded for the question "Name one Indian major carp", where 87.14 per cent (122 respondents) answered correctly with an average confidence level of 3.77. This finding suggests that the trainees effectively retained information related to commonly cultured fish species. Similarly, high levels of correct responses were observed for questions related to fish feed ingredients (80.71%), healthy pond water colour (83.57%), and feeding practices (77.14%), indicating successful knowledge retention in key areas of scientific fish culture. Muddam et al. (2020) [40] reported that training programmes on composite fish culture significantly improved the knowledge level of fish farmers regarding scientific aquaculture operations. In contrast, the lowest percentage of correct responses (66.43%) was recorded for the question "How much lime is required to correct the water pH?". Despite having the lowest correct response rate within this domain, the question received the highest average confidence level (3.98), suggesting that some trainees may have overestimated their understanding of liming requirements. The average confidence level across all statements was 3.68 on a five-point scale, indicating a relatively high degree of confidence among the trainees regarding their acquired knowledge. The confidence scores ranged from 3.49 to 3.98, with the lowest confidence observed for the question "Name one ingredient used in fish feed" and the highest confidence associated with liming requirements. Singh and Tanwar (2020) [52] , observed that a majority of trainees attained high levels of knowledge immediately after training. (Table 3 and Figure 2) Overall, the average frequency of correct responses for Layer 1 was 76.14 per cent, which is substantially higher than the pretest average score of 37.86 per cent. This improvement demonstrates that the training programme was effective in enhancing the trainees' recall and recognition of essential fisheries-related information. www.extensionjournal.com 4 indicate that the trainees exhibited a satisfactory level of conceptual understanding across the different aspects of fish farming. Among the statements, the highest percentage of correct responses was recorded for the question "Explain how low dissolved oxygen affects fish health", where 96.43 per cent (135 respondents) correctly explained the adverse effects of oxygen depletion on fish growth and survival. The average confidence level associated with this statement was 4.07, indicating a high level of certainty among the trainees regarding this concept. Masta and Janjhua (2023) [35] emphasized that self-efficacy plays a significant role in successful training outcomes and influences the application of learned practices in real-life situations. Further, Sumarwono et al. (2017) [54] found that training interventions significantly improved participants' selfconfidence alongside their knowledge and skills. High proportion of respondents correctly answered questions related to feeding time (85.00%) and the importance of liming in fish culture (81.43%), reflecting a sound understanding of essential pond management practices. Conversely, the lowest percentage of correct responses was observed for the statement "Why is lime added to fish ponds?", where only 57.14 per cent (80 respondents) provided a satisfactory explanation, accompanied by a relatively low confidence level of 3.50. The average confidence level across all statements was 3.80 on a fivepoint scale, which was higher than that observed in the recall and recognition layer (3.68). Similarly, Roy et al.

(2018) [50] observed that fisheries training programmes enhanced participants' comprehension of inland fisheries management concepts, enabling them to make informed management decisions. Muddam et al. (2020) [40] also reported that trained fish farmers exhibited a better understanding of pond management, feeding, and water quality management practices compared to their pre-training status. This finding indicates that the training programme not only enhanced factual knowledge but also improved the trainees' confidence in explaining scientific aquaculture concepts. Misra et al. (2024) [38] documented significant improvements in trainees' understanding of integrated fish farming concepts following structured training interventions. From Table 4 and Figure 2 Overall, the average frequency of correct responses in Layer 2 was 71.00 per cent, demonstrating a substantial level of conceptual learning among the trainees. Although the score was slightly lower than that of the recall and recognition layer (76.14%), it indicates that a majority of the respondents were able to comprehend and explain the scientific rationale behind key aquaculture practices. Explain the relationship between feed quality and fish growth. 84 (60.00%) 3.85 5.

Why is pond preparation important before stocking fish? 92 (65.71%) 3.79 6.

Why is lime added to fish ponds? 80 (57.14%) 3.50 7.

Why should healthy seed be selected for stocking? 90 (64%) 3.81 8.

What is the best time to feed fish? 119 (85.00%) 4.11 9.

Why is feeding done at fixed times? 85 (60.71%) 3.44 10.

Why is composite fish culture better than single species culture? 96 (68.57%) 3.93 Average: 99.4 (71.00%) 3.80

Layer 3: Applied Knowledge

Applied level is the last layer of Hierarchical Layered Knowledge Assessment (HLKA). It is to assess the trainees' ability to apply the knowledge and concepts acquired during the training programme to practical fish farming situations.

This layer represents the highest level of learning within the framework, as it evaluates decision-making and problemsolving abilities under real or simulated field conditions. A total of ten application-oriented questions were administered to the trainees after the completion of the training www.extensionjournal.com programme. The results presented in Table 5 indicate that the trainees demonstrated a moderate to high level of applied knowledge regarding scientific fish culture practices. Among the statements, the highest percentage of correct responses was recorded for the question "What steps will you follow to prepare a pond before stocking fish?", where 76.43 per cent (107 respondents) provided correct responses with the highest confidence level of 4.25.

Priyadharshini et al. (2017) [48] reported that participation in agricultural training programmes was positively associated with increased self-confidence among farmers. This finding indicates that the trainees acquired practical knowledge regarding pond preparation, which is a critical prerequisite for successful fish culture. Roy et al. (2018) [50] observed that while fisheries training programmes significantly improved farmers' knowledge and awareness, the application of scientific management practices required continuous guidance and practical exposure. Similarly, 71.43 per cent of the respondents correctly explained how they would apply the knowledge gained during training in their own ponds, suggesting a positive intention towards the adoption of scientific fish farming practices. Argade et al.

(2023) [4] similarly reported that skill development programmes enhanced farmers' technical competencies; however, practical adoption of scientific aquaculture practices depended on opportunities for hands-on learning and fieldlevel demonstrations. A substantial proportion of respondents (68.57%) were able to identify oxygen depletion problems and recommend appropriate corrective measures when fish were observed gasping at the water surface. Likewise, 67.14 per cent of the trainees correctly described the necessary management practices to be undertaken before stocking fish in a newly prepared pond. These findings indicate that the training programme successfully enhanced the trainees' practical understanding of pond management and water quality management practices. However, comparatively lower percentages of correct responses were observed for questions related to disease prevention (59.29%), correction of acidic pond water (60.71%), removal of unwanted fish from ponds (55.00%), and management of foul-smelling pond water (54.29%). Misra et al. (2024) [38] found that although trainees exhibited substantial knowledge gains following training, the successful application of scientific fish farming practices was influenced by experience, resource availability, and continued technical support. The relatively low confidence level associated with these questions further suggests that some trainees were uncertain about applying scientific recommendations under field conditions. From Table 5 and Figure 3 [4] observed significant positive improvements in fish farmers' knowledge following skill development training programmes conducted in Bihar. Singh and Tanwar (2020) [52] reported that vocational training programmes on integrated fish farming significantly enhanced trainees' knowledge levels, confirming the usefulness of knowledge gain measures in evaluating fisheries training programmes. The high relative knowledge gain demonstrates that the fisheries training programme was highly successful in improving the participants' understanding of scientific fish culture practices.

Training Impact Index (TII)

The Training Impact Index (TII) was computed to evaluate the overall effectiveness of the fisheries training programme by integrating post-training knowledge, confidence level, and knowledge gain. The calculated TII value was 58.66, indicating a moderately high level of training impact among the participants. De et al. (2019) [11] reported that composite measures provide a comprehensive assessment of training quality by simultaneously considering learning outcomes, professional relevance, and participant perceptions. Som et al. (2024) [53] concluded that composite indices are effective tools for quantifying the overall impact of training interventions and identifying areas requiring improvement. Paul et al. (2024) [46] reported that composite indices provide a more robust and policy-relevant assessment of training outcomes than single indicators alone.

Conclusion

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