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Essay: Learning analysis

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Learning Analysis

It is often acknowledged that there are various approaches or ways of learning and individuals prefer some particular method of interacting with, taking in, and processing stimuli or information to allow that individual to learn best (Werner & DeSimone, 2009, p. 164). This essay will aim to demonstrate that the idea of individualised learning styles, strategies, modes of learning, and rate of learning are important for the effective delivery of training. This paper is divided into two main parts: a literature review, and analysis of training. The literature review will support the view that there are various theories about training delivery methods and techniques, learning styles and strategies, and individual differences in the learning process. The training analysis will review parts of my previous job and the training needed to be proficient in that role. My training experience will also be discussed and why these training methods and techniques worked, what my learning strategy and style is, as well as my belief of how effective the training was. This essay will only focus on training delivered to me as a Human Resources Officer / Payroll Officer at The Prince Charles Hospital. It will be argued that there is no definitive way in which an individual may learn.

Literature Review

It is often argued what training method is best for an individual to learn at their best; however there are advantages and disadvantages within each method of training. The first and most common method of training, on-the-job training (OJT) uses more experienced and skilled employees to train less skilled and experienced employees, and can be used as a process for organisations to deal with applicants who do not possess the skills needed to perform the job (Scott, 1999; Salopek, 2004). Almost all one-on-one training can be regarded as OJT. However, some disadvantages of OJT are conducted informally, without structure, or planning (Werner & DeSimone, 2009, p. 167).

A more structured approach to training, the classroom training approach, is the second training method and usually takes place outside the normal workplace setting (Werner & DeSimone, 2009, p. 171), and has several benefits over OJT. Classroom settings have the ability to use a variety of training techniques and the environment can be designed to manage or minimise distractions, and can accommodate larger numbers of trainees thus permitting more economical delivery of training. However, there are two likely shortcomings of classroom methods including increased costs – such as travel to training facilities – and variation to the job setting, making transition of training more difficult (Werner & DeSimone, 2009, p. 171).

The third method of training is self-paced whereby it is either paper-based or computer-based training (CBT) in a non-classroom-based setting. The majority of self-paced learning utilises aspects from OJT combining with CBT. One advantage of CBT over other methods of training is its interactivity (Kearsley, 1984) and is well suited to on-demand trainees who need greater control over when and how training is delivered (Hartley, 2000; 2004). As research suggests, a combined approach is especially valuable when trainees have different learning styles and preferences (Werner & DeSimone, 2009, p. 166).

Training methods and techniques are also affected by an individual’s learning strategies and styles which can determine learning outcomes. A leading theorist on experiential learning argues that individuals have preferences over their learning style based on the person’s preferred modes of learning (Kolb D. A., 1984; 2005). Learning style represents how an individual’s choices made during the learning process affect what information is selected and how it is processed. Kolb (1984) argues that an individual’s learning style is based on the person’s preferred modes of learning, which is the individual’s orientation toward gathering and processing information during learning. Kolb (1984) also argues that an individual’s learning style often combines two modes of learning, and is developed as a result of life experiences as well as hereditary influences. Similarly, learning strategies are the techniques learners use to rehearse, elaborate, organise, and/or comprehend new material and influence self-motivation and feelings (Weinstein & Meyer, 1994; Birenbaum, 2007).

However, just as individuals have preferences about their learning styles and strategies, individuals also have preference for the sensory channels they use to obtain information. James and Galbraith (1985) propose seven primary perceptual preferences: print, visual, aural, interactive, tactile/manipulative, kinaesthetic or psychomotor, and olfactory. Also recently, Neil Fleming developed the VARK questionnaire, which is a scale measuring one’s preference for taking in and putting out information in a learning context (Fleming, 2010). Four preferences measure this by scale are: visual, aural, read/write, and kinaesthetic.

In addition to the learning styles and strategies of an individual, people learn at different rates. A useful way to show rates of learning is by drawing learning curves, which can provide useful feedback to both the trainers and trainees, and changes to the training program may need to be implemented to improve the rate of learning (Kostiuk & Follmann, 1989). Also some methods of training may be better suited to certain types of people. Thus, research on attribute-treatment interaction (ATI) has sought to develop training systems that can be adapted to differences between individual learners (Cronbach, 1967; Cronbach & Snow, 1977). Two variables that have received considerable attention in ATI research are cognitive ability and motivation (Vroom, 1964). However, to date research has found little conclusive evidence of an interaction between motivation and ability (Terborg, 1977). Though, research suggests that ability and self-efficacy are better predictors of performance in the early stages of skill acquisition, whereas motivation is a better predictor of performance during later stages (Kanfer & Ackerman, 1989; Kanfer, Ackerman, Murtha, Dugdale, & Nelson, 1994; Mitchell, Hopper, Daniels, George-Falvy, & James, 1994).

Analysis of Training

My position as a Human Resources Officer / Payroll Officer at The Prince Charles Hospital, involved knowledge of relevant payroll legislation in order to perform the functions of payroll, as well as perform other administrative duties. This included in-depth knowledge of applicable awards, and relevant acts, as well as taxation and superannuation law. In addition, updates on enterprise bargaining agreements were to be studied, as well as industrial relations manuals, which were used as supplementary conditions if not prescribed in the awards. Understanding the relevant legislation was essential to perform a satisfactory job in payroll, as well as comprehend the human resource information management system (HRIMS), called LATTICE that was used to automate some functions of payroll. Many conditions of the awards and acts were translated into codes into LATTICE and this is where the knowledge of payroll legislation was applied.

As research suggests, not one training method is best, and the effective delivery of training and development programs requires considerable preparation, as well as creativity and flexibility (Korte, 2006). The training required for payroll legislation and LATTICE involved OJT, self-paced learning, and classroom facilitated training, over a period of four weeks. Training that was conducted on the job, was performed by a co-worker who instructed me what payroll legislation was relevant, basic functions of LATTICE, and what daily administration housekeeping was needed to be completed. This training method was appropriate to me as the information was relevant, and the transfer of knowledge required to perform the duties of the job was immediate as I could instantly practice the work tasks. Also, my learning was enhanced because the learning environment was the same as the working environment. This is supported by research that acknowledges these two major advantages of OJT: immediate transfer of training to the job, and learning environment similarity (Rothwell & Kazanas, 1994). I also was instructed to learn the particulars of all current payroll legislation by self paced reading predominantly at work.

In addition, more formal and structured training was conducted on LATTICE at a training facility owned by The Prince Charles Hospital. The training was conducted offsite at a computer-based learning centre in a classroom setting, where scenarios were given to the class to practice the functions of LATTICE. Learning all the functions of LATTICE thoroughly in a setting where there were no interruptions and focused on set objectives, created an efficient and effective learning environment. Lecture techniques were also utilised, and I found this effective when learning basic facts on LATTICE; however this is often criticized by academics as it can lead to only one-way communication, and negative behaviour, such as passivity and boredom (Korman, 1971; Ramsey & Fitzgibons, 2005).

As theorists suggest, an individual’s learning style can affect training methods and techniques (Mitchell & Honore, 2007; Cronbach, 1967). Although a mix of methods and techniques were used in my training, I am more inclined to learn by thinking about an issue in theoretical terms, and learn by doing. This was evident in my training in payroll legislation as I was able to theorize what award conditions were applicable to whom, and when these conditions are to be applied, and I only fully comprehended the training when I performed what I had learnt. As Kolb (1984) suggests, there are four basic modes of experiential learning: concrete experience – learning by feeling; abstract conceptualization – learning by thinking; reflective observation – learning by watching; and active experimentation – learning by doing. Therefore it is evident that I have a strong preference towards abstract conceptualization and active experimentation.

However, Kolb (1984) further suggests that an individual combines two modes of learning and is identified in four learning styles: divergent – a combination of feeling and watching; assimilation – a combination of thinking and watching; convergent – a combination of thinking and doing; and accommodative – a combination of feeling and doing. As my preference is to learn by thinking and doing, it is obvious that I have a convergent learning style whereby I have a focus on problem solving, decision making, and the practical application of ideas. Similar to Kolb’s modes of learning, learning strategies represent the behaviour and thoughts an individual engages in during learning (Weinstein & Mayer, 1986). Weinstein & Mayer (1986) suggest five learning strategies: rehearsal strategies; elaboration strategies; organisational strategies; comprehension strategies; and affective strategies. Highlighting or underlining text, copying notes, and repeating items on a list are trademarks of a rehearsal learning strategy, and elaboration strategies involves forming a mental image, taking notes and paraphrasing. I found these two learning strategies best in my training as I learnt by highlighting text in payroll legislation, and taking notes when in LATTICE training.

As mentioned previously, James and Galbraith (1985) suggest seven sensory channels that an individual may give preference when learning. Fleming (2010) who developed the VARK questionnaire incorporated most of these seven sensory channels into four scalable categories: visual, aural, read/write, and kinaesthetic. I undertook this questionnaire and found that I predominantly favour kinaesthetic learning, followed by aural and visual, where read/write was my least preference of learning. The VARK questionnaire revealed that various strategies – on the intake of information, the best way to retain information, and how to recall information when needed – depend on your preference for learning. As I am predominantly a kinaesthetic learner, the VARK questionnaire suggests taking in information by doing, using exhibits or examples, and lecturers who give real life examples. It also suggests the best way to retain this information is by using examples or case studies, and the best way to recall information in an exam or test is to practice the answers.

In addition to learning styles and strategies, individuals learn at different rates. My kinaesthetic learning preference as revealed in the VARK questionnaire is evident in my convergent learning style as suggested by Kolb (1984), and rehearsal and elaboration learning strategies as proposed by Weinstein & Mayer (1986). I was able to grasp the concepts of LATTICE quite quickly. However, I was slower to learn the details of payroll legislation, as read/write was my least preferred learning method according to the VARK questionnaire. My overall rate of progress can be described as starting slowly but steadily improves to a high level of performance. The mix of training delivery methods and techniques were a good combination for me as I was able to learn by doing and thinking, and apply what I had learnt by using what-if theories and case studies. In my opinion, most individuals would also be able to employ their preferences for learning successfully.

There are various training delivery methods and techniques, numerous learning styles and strategies an individual may prefer, as well as different rates of progress for an individual. By understanding what my learning preferences are, I am able to maximise a learning outcome by employing specific learning styles and strategies when different training delivery methods and techniques are used. By reviewing relevant literature and an analysis of my training has led me to conclude that there is no single or best way to conduct training, but it is best to combine approaches to training delivery methods as trainees have different learning styles and preferences.

References

Birenbaum, M. (2007). Assessment and Instruction Preferences and their Relationship with Test Anxiety and Learning Strategies. Higher Education , 53 (1), 749-768.

Cronbach, L. J. (1967). How Can Instruction be Adapted to Individual Differences? In R. M. Gagn� (Ed.), Learning and Individual Differences. Columbus, Ohio: Charles E Merrill.

Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and Instructional Methods. New York: Irvington.< /p>

Fleming, N. D. (2010). Frequently Asked Questions. Retrieved April 2, 2010, from VARK: A Guide to Learning Styles: http://www.vark-learn.com/english/page.asp?p=faq

Hartley, D. E. (2000). On-Demand Learning: Training in the New Millenium. Amherst, Massachusetts: HRD Press.

Hartley, D. E. (2004). The Need for Speed. Training and Development , 58 (10), 22-23.

James, W., & Galbraith, M. W. (1985, January). Perceptual Learning Styles: Implications and Techniques for the Practitioner. Lifelong Learning , pp. 20-23.

Kanfer, R., & Ackerman, P. L. (1989). Motivation and Cognitive Abilities: An Integrative/Aptitude-Treatment Interaction Approach to Skill Acquisition. Jornal of Applied Psychology , 74 (1), 657-690.

Kanfer, R., Ackerman, P. L., Murtha, T. C., Dugdale, B., & Nelson, L. (1994). Goal Setting, Conditions of Practice, and Task Performance: A Resource Allocation Perspective. Journal of Applied Psychology , 79 (1), 826-835.

Kearsley, G. (1984) . Training and Technology. Reading, Massachusetts: Addison-Wesley.

Kolb, A. Y., & Kolb, D. A. (2005). Learning Styles and Learning Spaces: Enhancing Experiential Learning in Higher Education. Academy of Management Learning and Education , 4 (2), 193-212.

Kolb, D. A. (1984). Experiential Learning. Englewood Cliffs, New Jersey: Prentice Hall.

Korman, A. K. (1971). Industrial and Organisational Psychology. Englewood Cliffs, New Jersey: Prentice Hall.

Korte, R. F. (2006). Training Implementation: Variations Affecting Delivery. Advances in Developing Human Resources , 8 (4), pp. 514-527.

Kostiuk, P., & Follmann, D. (1989). Learning Curves, Personal Characteristics, and Job Performance. Journal of Labor Economics , 7 (2), 129-146.

Mitchell, A., & Honore, S. (2007). Criteria for Successful Blended Learning. Industrial and Commercial Training , 39 (3), 143-149.

Mitchell, T. R., Hopper, H., Daniels, D., George-Falvy, J., & James, L. R. (1994). Predicting Self-Efficacy and Performance During Skill Acquisition. Journal of Applied Psychology , 79 (1), 506-517.

Ramsey, V. J., & Fitzgibons, D. E. (2005). Being in the Classroom. Journal of Management Education , 29 (2), 333-356.

Rothwell, W. J., & Kazanas, H. C. (1994). Improving On-The-Job Training. San Francisco: Jossey-Bass.

Salopek, J. J. (2004). Balancing Work and Learning. Training and Development , 58 (7), 16-18.

Scott, J. (1999). Employees get OJT . Memphis Business Journal , 21 (1), 1-3.

Terborg, J. R. (1977). Validation and Extension of an Individual Differences Model of Work Performance. Organizational Behaviour and Human Performance , 18, pp. 188-216.

Vroom, V. H. (1964). Work and Motivation. New York: Wiley.

Weinstein, C. E., & Mayer, R. F. (1986). The Teaching of Learning Strategies. In M. C. Wittrock (Ed.), Handbook of Research on Teaching (3rd ed., pp. 315-327). New York: Macmillan.

Weinstein, C. E., & Meyer, D. K. (1994). Learning Strategies, Teaching and Testing. In T. Husen, & T. M. Postlethwaite (Eds.), The International Encylopedia of Education (2nd ed., pp. 3335-3340). Oxford: Pergamon Press.

Werner, J. M., & DeSimone, R. L. (2009). Human Resource Development. Mason, Ohio: South-Western Cengage Learning.