This document discusses training needs assessments (TNAs). A TNA identifies educational activities employees need to improve productivity by focusing on needs rather than desires. Conducting a TNA helps determine if training will impact employee productivity, identifies specific training needs, and differentiates training needs from other organizational issues. Common TNA tools include interviewing managers and employees, conducting focus groups and surveys, and reviewing competencies, development plans, and strategic plans. While not all tools need to be used, it is recommended to use at least three data sources, with at least one being participatory like focus groups or surveys. TNAs should be conducted periodically as organizational and employee needs change over time.
Kabaddi is a contact team sport of Indian-origin. It is a highly strategic game and generates a significant amount of data due to its rules. However, data generated from kabaddi tournaments has so far been unused, and coaches and players rely heavily on intuitions to make decisions and craft strategies. This paper provides a quantitative approach to the game of kabaddi. The research derives outlook from an analysis performed on data from the 3rd Standard-style Kabaddi World Cup 2016, organised by the International Kabaddi Federation. The dataset, which consists of 66 entries over 31 variables from 33 matches, was manually curated. This paper discusses and provides a quantitative perspective on traditional strategies and conceptions related to the game of kabaddi such as attack and defence strategies. Multiple hypotheses are built and validated using student’s t-test. This paper further provides a quantitative approach to profile an entire tournament to gain a general understanding of the strengths of various teams. Additionally, team-specific profiling, through hypotheses testing and visualisation, is presented to gain a deeper understanding of team’s behaviour and performance. This paper also provides multiple models to forecast the winner. The model-building includes automatic feature selection techniques and variable importance analysis techniques. Generalised linear model with and without an elastic net, recursive partitioning and regression tree, conditional inference tree, random forest, support vector machine (linear and radial) and neural network-based models are built and presented. Ensemble models use generalised linear model and random forest model techniques as ensemble method to combine outcome of a generalised linear model with the elastic net, random forest, and neural network-based models. The research discusses the comparison between models and their performance parameters. Research also suggests that ensemble technique is not able to boost up accuracy. Models achieve 91.67%-100% accuracy on cross-validation dataset and 78.57%-100% on test set. Results presented can be used to design in-game real-time winning predictions to improve decision-making. Results presented can be used to design agent and environments to train artificial intelligence via reinforced learning model.
Kabaddi is a contact team sport of Indian-origin. It is a highly strategic game and generates a significant amount of data due to its rules. However, data generated from kabaddi tournaments has so far been unused, and coaches and players rely heavily on intuitions to make decisions and craft strategies. This paper provides a quantitative approach to the game of kabaddi. The research derives outlook from an analysis performed on data from the 3rd Standard-style Kabaddi World Cup 2016, organised by the International Kabaddi Federation. The dataset, which consists of 66 entries over 31 variables from 33 matches, was manually curated. This paper discusses and provides a quantitative perspective on traditional strategies and conceptions related to the game of kabaddi such as attack and defence strategies. Multiple hypotheses are built and validated using student’s t-test. This paper further provides a quantitative approach to profile an entire tournament to gain a general understanding of the strengths of various teams. Additionally, team-specific profiling, through hypotheses testing and visualisation, is presented to gain a deeper understanding of team’s behaviour and performance. This paper also provides multiple models to forecast the winner. The model-building includes automatic feature selection techniques and variable importance analysis techniques. Generalised linear model with and without an elastic net, recursive partitioning and regression tree, conditional inference tree, random forest, support vector machine (linear and radial) and neural network-based models are built and presented. Ensemble models use generalised linear model and random forest model techniques as ensemble method to combine outcome of a generalised linear model with the elastic net, random forest, and neural network-based models. The research discusses the comparison between models and their performance parameters. Research also suggests that ensemble technique is not able to boost up accuracy. Models achieve 91.67%-100% accuracy on cross-validation dataset and 78.57%-100% on test set. Results presented can be used to design in-game real-time winning predictions to improve decision-making. Results presented can be used to design agent and environments to train artificial intelligence via reinforced learning model.
Training needs analysis, skills auditing, training evaluation, calculating training ROI and strategic learning and development best practice principles and processes
Management Training requires Assessment and Analysis which is explained in Effective HR. This presentation explains the significance of ‘needs analyses’ in training. Understand various types of training needs and the processes involved in Training Analysis, know the components of a training Needs Assessment and the methods for collecting data.
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Training need assessment, Meaning, Why Training need assessment, Benefits of training need assessment, Types of need analysis, Key steps involved in conducting effective training need assessment, Steps of training need assessment.
Training Needs Analysis (TNA) is the process in which the company identifies training and development needs of its employees so that they can do their job effectively.
Training & developing employees
orienting employees
purpose of orienting
the orientation process
the training process
why do you company trains
Benefits of training
T&D process
Training needs analysis, skills auditing, training evaluation, calculating training ROI and strategic learning and development best practice principles and processes
Management Training requires Assessment and Analysis which is explained in Effective HR. This presentation explains the significance of ‘needs analyses’ in training. Understand various types of training needs and the processes involved in Training Analysis, know the components of a training Needs Assessment and the methods for collecting data.
For more such innovative content on management studies, join WeSchool PGDM-DLP Program: http://bit.ly/SlideShareEffectHR
Join us on Facebook: http://www.facebook.com/welearnindia
Follow us on Twitter: https://twitter.com/WeLearnIndia
Read our latest blog at: http://welearnindia.wordpress.com
Subscribe to our Slideshare Channel: http://www.slideshare.net/welingkarDLP
Training need assessment, Meaning, Why Training need assessment, Benefits of training need assessment, Types of need analysis, Key steps involved in conducting effective training need assessment, Steps of training need assessment.
Training Needs Analysis (TNA) is the process in which the company identifies training and development needs of its employees so that they can do their job effectively.
Training & developing employees
orienting employees
purpose of orienting
the orientation process
the training process
why do you company trains
Benefits of training
T&D process
2. What is a TNA?
• Identification of educational activities
employees need to improve their
productivity
• Focus is on needs, not desires
• A form of gap analysis
3. Why conduct one?
• Determine whether training will make a
difference in employee productivity
• Decide what specific training each employee
needs and what will improve their job
performance
• Differentiate between the need for training and
other organizational issues—and there are
always other organizational issues!
4. Commonly-used tools
• Interview managers
– Obtain info on upcoming projects, organizational goals
– Managers identify gaps
• Interview employees
– Find out what would make their job easier (“I could do this job better
if…”)
– Top performers can tell you what they learned, and how
• Conduct focus groups
• Conduct surveys
• Review any existing competencies developed for jobs
• Review employee development plans/performance reviews
• Review strategic plan and mission statement
5. Do I have to use them all?
• No
• Rule of thumb: use at least three data
sources
• Make at least one data source
participatory—that is, involve the staff
whose needs you are trying to meet
– Focus groups, surveys
6. Focus Groups
• Classic methodology
– Script from CLEM c. mid-1980’s
– Can structure groups to meet a variety of
needs (mixed groups v. job-specific)
– Can use results to develop surveys for all staff
• Copies of script available for everyone
– We’ll walk through the technique: experiential
learning ;-)
7. Surveys
• Plug data into Zoomerang, Survey Monkey, or
similar tool and email to staff (view sample)
– Alert them it’s coming
• Collect only basic demographic data
– Some degree of anonymity is important to some
• Share compiled results widely
– Identify needs you plan to address centrally
– At CCPL, management groups provide in-service
training
– Supervisors can be alert to other opportunities
8. How Often?
• It depends…
– What are you looking for?
– What are you going to do with the data?
– How recent is your strategic plan?
– How fresh is your performance review data—and do
staff complete an IDP?
• Things change rapidly these days
• Sometimes discovery of those other
organizational issues is important!