Artificial Intelligence and Approaches to Music Education

Artificial Intelligence and Approaches to Music Education Abstract The goal of this paper is to review the principal approaches to Music Education with a focus on Artificial Intelligence (AI). Music is a domain which requires creativity, problem-seeking and problem-solving respectively, from both learner and teacher, therefore is a challenging domain in Artificial Intelligence. It is argued that remedial intelligent tutoring-systems are inadequate for teaching a subject that requires open-ended thinking. Traditional classroom methods are sometimes favoured because tutors can focus on individual differences and enhance creativity and motivation. However, it can also be argued that AI is a mechanism which enables those without traditional musical skills to ‘create’ music. Almost the only goal that applies to music composition in general is ‘compose something interesting’ (Levitt, 1985). This paper will review different approaches to AI in Music Education. Approaches considered will be: Intelligent Tutoring Systems in Music; AI based Music Tools; highly interactive interfaces that employ AI theories. 1. Introduction This paper will review some of the approaches to using Artificial Intelligence in Music Education. This particular field is of high interdisciplinary and involves contributions from the fields of education, music, artificial intelligence (AI), the psychology of music, cognitive psychology, human computer interaction, philosophy, computer science and many others. AI in education itself is a very broad field, dating from around 1970 (Carbonell, 1970) and has its own theories, methodologies and technologies. For brevity, we will abbreviate Artificial Intelligence in Education to AI-ED, following a standard convention. Definitions The scope of AI in Education (AI-ED) is not decisive, so it will be useful to consider some definitions. A common definition is: any application of AI techniques or methodologies to educational systems. Other definitions which focus more narrowly are, for example: any computer-based learning system which has some degree of autonomous decision-making with respect to some aspect of its interaction with its users (Holland, 1995). This definition suggests the requirement that AI techniques reason with the user at the point of interaction. This might be in relation to best teaching approach, the subject being taught or any misconceptions or gaps in the student’s knowledge. However, AI-ED in a wider context is sometimes defined as: ‘the use AI methodologies and AI ways of thinking applied to discovering insights and methods for use in education, whether AI programs are involved at the point of delivery or not’ (Naughton, 1986). In practice, these contrasting approaches form a continuum. Music: An open-ended domain A useful distinction in AI-ED is between formalised domains and the more open-ended domains (‘domain’ means subject area to be taught). In relation to domains such as mathematics and Newtonian dynamics there are clear targets, correct answers and a reasonable clear and concise structure to follow for success. Whereas in open-ended domains such as music composition, there are in general, no clear goals, no set criteria to follow and no correct answers. The focus is based upon, as mentioned earlier, ‘Compose something interesting’ (Levitt, 1985). Rittel and Webber (1984) describe this particular problem in domains as ‘wicked problems’. In such domains there cannot be a definitive formulation for the problem or the answer. Wicked domains such as music composition require learners to not just solve problems but also seek problems (Cook, 1994). The term problem seeking is used in a number of disciplines such as animal behaviour (Menzel, 1991). Cook (1994) imported the term into AI in Education in particular reference to the sense of philosopher Lipman (1991). In this sense Cook (1994) refers to the term ‘problem seeking’ as follows: Problems are treated as ill-defined and open-ended There is a continual intertwining of problem specification and solution Criteria for completion is very limited Context greatly affects the interpretation of the problem Problems are always open re-interpretation and re-conceptualisation In relation to expressive performing arts and music composition there is no goal or problem to be solved. The learner must find or create goals and problems which then may need to be revised, modified and rejected where best suited to his/her taste. 2. Computer-Aided Instruction It is worth considering briefly the music education programs that negligibly use AI as a background to AI approaches in education. Historically, computers used in music, and most other subjects, were associated with the theory of learning behaviourism. These particular systems (branching teaching programs) stepped through the following algorithm (O’Shea and Holland, 1983), Present a ‘frame’ to the student i.e. Present the student with pre-stored material (textual or audio visual) Solicit a response from the student Compare the response with pre-stored alternative responses Give any pre-stored comment associated with the response Look up the next frame to present on the basis of the response An example of this kind of system was the GUIDO ear-training system (Hofstetter, 1981). Branching teaching programs tend to respond to the user in a manner that has more or less been explicitly pre-planned by the author. Therefore, this tends to limit the approach to a simple treatment. Multimedia and Hypermedia Multimedia and hypermedia has had a great impact on music education and transformed music education software programs, giving a different emphasis from the earlier behaviourist programs. Recent educational music programs such as Seventh Heaven, Ear Trainer, Interval and Listen aim to provide practice in recognising or reproducing intervals, chords or melodies. MacGAMUT is a classroom simulation program that dictates exercises and provides a detailed marking scheme. Other programs such as MiBAC Music Lessons, Perceive and Practica Musica offer a comprehensive ear training program including scales, durations, modes and tuning. See Yavlow (1982) for information on the aforementioned programs. Since the domain is relatively clear-cut and non-problematic, ear training and music theory are popular methods in non-AI music education programs. There are many useful musical computer tools applicable to education such as music editors, sequencers, computer-aided composition tools, multimedia reference tools on CD-ROM Masterworks and much more. 3. Intelligent Tutoring Systems for Music: A ‘Classical’ Approach The history of AI in education can be divided into two periods, the ‘classical’ period (1970 – 1987) and the ‘modern’ period (1987 to present day). In the classical period, the three component ‘traditional’ model of an Intelligent Tutoring System (ITS) was the most common and influential idea. This model was sometimes extended to a four component model. After 1987, ideas had shifted to finding alternative ways around the traditional model. However, this was limited due to research available at those times, and the traditional model remains influential and is still used to the present day. Each of the three components of the traditional model can be considered a separate ‘expert’ system’. The traditional ITS model (Sleeman and Brow, 1982) consists of three AI components, each an expert in its own area. The first component, the domain model, is an expert in the subject being taught. So in the case of a vocal tutor, the domain expert itself would be able to perform vocal tasks. This requirement is essential if the system is to be able to answer unforeseen questions in relation to the task in hand. The second component is the student model. Its purpose is to build a model of the student’s knowledge, capabilities and attitudes. This will allow the system to vary its approach in accordance to the individual student. In essence, the student model can be viewed as a checklist of skills. This is sometimes modelled as an overlay i.e. a tick list of the elements held in the domain. Sophisticated models may view it as a deliberately distorted element or a faulty ‘expert’ system. These errors are intended to mirror a student’s misconceptions. A fair diagnosis of a student’s knowledge, skills, capabilities and beliefs is often a hard problem in AI. One partial way around the diagnosis problem would be to ask the student about their capabilities, beliefs, previous experience and so on. A more stringent approach is to set the student tasks specifically designed to analyse their skills. The results can then be used to construct the student model. The third component of the traditional ITS model is the teaching model. Typically, this may consist of teaching strategies such as Socratic tutoring, coaching and teaching by analogy (Elsom-Cook, 1990), to simply allowing the student to explore available materials unhindered, with or without the guidance of a human teacher. The fourth component is an interactive user interface for the tasks mentioned, if it is used. Note that not all Intelligent Tutoring Systems consist of all three components. It is common to have a central focus on one maybe two components, and omit, or greatly simplify the others. In particular, most ITS’s in music focus on the expert or student model. Irrespective of the emphasis, ITS models require an explicit, formalisable knowledge of the task. However, many skills in music correspond to wicked problems and are resistant to explicit formalisation. This narrows the number of areas ITS models can be applied to in music education. An example area is Harmonisation. It is one of the few musical topics for which relatively detailed, rules of thumb can be found in a textbook. But even here, the traditional ITS model may not be effective. There are two systems from the classical ITS period, which are good examples of the potential and limitations of the ITS approach in music, Vivace and Macvoice. 3.1 Vivace: An expert system Vivace is a four-part chorale writing system, created by Thomas (1985). Vivace is not an ITS model in itself, yet has formed the basis of one. It takes an eighteenth century chorale melody and writes a bass line and two inner voices that fit the melody. It uses text from books, abstracted from the practice of past composers, to employ rules and guidelines for harmonisation. These rules can be categorized into four types: firm requirements, preferences, firm prohibitions, less firm prohibitions. There are three specific problems which can be identified for any human or machine when trying to harmonise on the basis of the rules. The first problem is indeed common in beginners’ classes, to satisfy all the formal rules and produce a composition which is correct but aesthetically unsatisfactory. The second problem is that most of the guidelines are prohibitions rather than positive suggestions. Milton Babbit observes that ‘the rules†¦are not intended to tell you what to do, but what not to do’ (Pierce, 1983). In other words, if we view harmonisation as a typical AI ‘generate and test’ problem, the rules constitute weak help in the testing phase, but little help in well focused generation. The third problem is that it is quite impossible to satisfy all of the preferences at any one given time. Some preference rules may have to be broken. A clear order of importance of preference rules is not assigned by traditional descriptions in fact, it is not at all clear that any fixed order would make sense. However, it is possible to write a rule-based system that implements text book rules. In principle, a traditional ITS system can use these rules to criticise student’s work and serve as a model of the expertise they are supposed to acquire. In relation to the limits aforementioned, how useful or effective would such a tutor be? Thomas used the tutor to illuminate the limitations of the theory. By using Vivace, Thomas was able to establish that text book rules are an inadequate characterisation when performing such a task at expert level. Thomas discovered using only conventional rules about range and movement the tenors voice would most certainly move to the top of its range and stay there. Thomas suggested that there must be a set of missing rules and metra-rules to fill theses gaps. He used a Vivace experimental tool to establish this gap. In each experiment Thomas had to use his intuition to decide upon whether the results were musically viable or not. Thomas discovered that many of the traditional rules were overstated or needed redefining. He also unveiled new guideline and was able to understand the task at a more strategic level. With the assistance f her human pupils, Thomas formulated a number of heuristics for ‘what to do’ rather than ‘not what to do’. Experiments with Vivace enabled Thomas to realise the need to make human pupils aware of high level phase structure prior to detailed chord writing. As a result of her experiments, Thomas was able to use her new knowledge about the task, as a result of ‘teaching’ her expert system, and write a new teaching text book based on her findings. Part of this knowledge was used in a simple commercial ITS, which criticises student’s voice-leading (MacVoice). 3.2 MacVoice MacVoice criticises voice-leading aspects of four part harmonisation. It is a Macintosh program based on the expert system Vivace. The MacVoice also includes a music editor as part of its interface. MacVoice makes it possible to input any note, any chord at a time or a voice at a time, or notes in any disconnected fashion. As soon as a note is placed on the stave, it will display its guess as to the function of the corresponding chord in the form of an annotated Roman numerical. Three are two important limitations of this system as follows: firstly, all chords must form Homophonic blocks (all notes must be of the same duration); and secondly, the piece must be in a single key. There is one other menu function, called ‘voice-leading’.This particular function inspects the harmonisation in line with a set of base rules for voice-leading, indicating any errors. MacVoice is quite flexible to use. MacVoice has been used practically at Carnegie Mellon University. MacVoice does not give positive strategic advice. It only points out errors. It does not address the efficiency or any other benefits of the chord sequences involved. Further research on this topic may include a visual display of what the voice-leading constraints are, or the possible preferred outcomes. 3.3 Lasso Lasso was formalised by Lux (1725). It is an intelligent tutoring system designed for the 16th century counterpart and is limited to two voices. Newcomb’s approach focuses on intending to provide simple and consistent guidelines to help students know what is required to pass exams. The process of codification of the necessary knowledge goes beyond that of text book rules and guidance. Like Thomas, Newcomb was aware of this, however, approached it using a probabilistic manner, analysing scores to find out such facts as ‘the allowable ratio of skip to non-skip melodic intervals’ and ‘how many eighth note passages can be expected to be found in a piece of a given length’ (Newcomb, 1985). Also, the knowledge used for criticising students work is being coded as branch procedural code. There are also unvarying canned error messages, help messages and congratulatory messages. This will assist students, offering some form of motivation. Lasso is a very impressive system. It has a quality musical editor, tackles complex musical paradigm and has been used in real teaching contexts. However, there are some intrinsic problems. The rules are at a very low level, and there are a high number of them. There is a system rule which prevents over one hundred comments being made about any one given attempt to complete an exercise. For example, typical remarks made by Lasso include; â€Å"A melodic interval of a third is followed by stepwise motion in the same direction.† â€Å"Accented quarter passing note? The dissonant quarter note is not preceded by a descending step.† (Newcomb, 1985). The quantity of relevant text required to put in help context of myriad low-level criticisms could easily overwhelm students. Students complained that it was so difficult to meet Lasso’s demands that they were forced to revise the same task repeatedly. A solution to this problem would be to incorporate general principles to govern the low-level rules. Using such codified principles will reduce the number of comments required to relevant text and generalise observations. 3.4 Concluding remarks on Intelligent Tutoring Systems: A ‘Classical Approach’ The traditional Intelligent Tutoring System approach assumes an objectivist approach to knowledge. Such systems depend on the assumption there is a well-defined body of knowledge to be taught and can be put into precise concepts and relationships. This works with four-part harmonisation and 16th century counterparts. However, in a more open-ended context, an objectivist approach can be very limited. In domains which are artificially limited, teaching of rules drawn from practical experience tends not be a very good approach. Using verbal definitions to teach a musical concept is limited and does not compare to the knowledge required to identify the true meaning of these definitions to be an experienced musician. It is all very well to define a chord, a dominant eighth in terms of its interval pattern and provide general rules but to an experienced musician the ‘meaning’ of a chord or a dominant eight is much more depending on the context. Being able to intelligently manipulate structures is far more important than to just being able to understand and obey a set of rules, which an experienced musician will be capable of doing so. Rather than just a set of explanations, a student needs a structured set of experiences making them more aware of musical structures, being able to manipulate them intelligently and most importantly, more capable of formulating sensible musical goals to pursue. 4. Open-ended Microworlds: The Logo Philosophy A contrasted idea from the classical approach of AI in education, which is just as influential as the notion of an ITS is the Logo approach (Papert, 1980). The Logo philosophy has particular attractions to open-ended domains such as music. It focuses its approach on the idea of an educational microworld. An educational microworld is an open-ended environment for learning. Therefore, there are no specific built-in lessons. The Logo approach in associated microworlds does not need to involve much, or indeed any AI at point of delivery. However, their designs tend to be strongly influenced by AI methodologies and tools. A simple version of AI programming language is used to build microworlds. Students are encouraged to write or modify programs as a means of exploring the domain. Logo doubles as the name of programming language based on Lisp, used for just this purpose. There are three distinct elements in the Logo approach: Logo (and similar languages) as a programming tool; Logo as a vehicle for expressing various AI theories for educational purposes; and Logo as an educational philosophy. Firstly, we will briefly explore Logo as an educational philosophy. In its early work, Logo was mainly used for mathematics learning, poetry and music. One of the versions encouraged children to produce new melodies by rearranging and modifying melodic phrases. The learning philosophy was aimed to enable children to have a better understanding of the concept by making them envision or pre-hear a result. Thus, enabling them to work out how to achieve it, and realise the reason behind obtaining an unexpected result. This learning philosophy was derived from a number of sources, including the psychologist Piaget’s notions of how children construct their own knowledge through play. The Logo approach in relation to microworlds can be somewhat complex. Students are sometimes provided with a simplified version of an AI model in some problem domains. For example, in the case of music composition, fragments of illustrative material can be generated using generative grammars as models of particular composition techniques. The supplied programs can be used by students to explore, criticise, and refine their own (or someone else’s) model of process. Notice that none of the three components in the ITS model are required in the Logo approach. In practice, students need some form of guidance from teachers in order to make use of their full potential using Logo systems. If there is no guidance from a teacher the students risks only learning a technique without appreciating the wider possibilities and understanding the true meaning of being an experienced musician. The educational philosophy associated with Logo has been applied to a number of systems in music at different levels and in different ways, as mentioned below. 4.1 Music Logo System: Bamberger’s System Jeanne Bamberger’s Music Logo System (1986, 1991) can be used to work with sound cards or synthesisers. It uses programming elements called functions to structure and control musical sounds. Music Logo’s central data structure is a list of integers representing sequences of durations and pitches, which can be stored separately. These can be manipulated separately before being played by a synthesiser. So for example, to play A above middle C for 30 beats, then middle C for 20 beats, then G for 20 beats , the following expression might be used. Play [a c g] [30 20 20] Programming constructs such as repeat can easily be understood by beginners to do musical work. Using arithmetic and list manipulation functions, note and duration list can be manipulated separately. Features such as recursion and random number generators can be used to build complex musical structures. Common musical operations are provided (list manipulation functions). For example, one function takes a duration a pitch list and generates a number of repetitions of the phrase shifted at each repetition by a constant pitch increment, creating a simple sequence (in a musical sense of the term). Bamberger’s Music Logo System also provides other musical functions, such as retrograde (reverses a pitchlist), invert (processes a pitch list to the complimentary values within an octave), and fill (makes a list of all intermediate pitches between two specified pitches). To try and guess a musical outcome, manipulate lists and procedures or conversely iteratively manipulating lists of representations to try to reproduce something previously imagined, Bamberger suggests many simple exercises. These techniques, in many ways, are a reflection of educational techniques suggested by Laurillard (1993) for general use in higher education. There are two particular classes of phenomena suggested by Bamberger, which emphasises the importance of ‘shock’ and learning experiences. Firstly, perceptions of phrase boundaries occur in melodic and rhythmic fragments dependent upon small manipulations of the duration list. Secondly, there is an unpredictable difference between degree of change in the data structure and the degree of the perceived change produced. In priniciple, the Logo system allows students to focus on manipulating any kind of musical structuring technique. However, in practice the focus tends to be on simple, small scale structures such as motives, and their transformation. 4.2 A series of microworlds: Loco Peter Desain and Henkjan Honing developed a series of microworlds and tools applying the Logo philosophy. The first series was the LOCO (Desain and Honing, 1986, 1992). The second was POCO (Honing, 1990), followed by Expresso (Honing, 1992) and LOCO-Sonnet (Deasin and Honing, 1996). All of these microworlds carefully reflect the thought behind AI methodologies and how they can be applied to music education. LOCO is similar to Bamberg’s Logo, in the sense it also focuses on music composition. The central component is a set of tools for representing sequences of musical events, which can be interfaced with any output device or instrument. It is also flexible enough to take input from practically any composition system. Microworlds provided each offer tools for useful style-independent composition techniques, particularly stochastic processes and context free music grammars. Two musical objects provided essentially are just ‘rests’ and ‘notes.’ LOCO’s time structuring mechanism is simple and elegant. There two relations, Parallel and Sequential – used to combine arbitrary musical objects. Sequential is a function which causes musical objects in an argument list to be played one after another, whereas, Parallel is a function that causes arguments to be played simultaneously. It is quite simple to nest a parallel structure within a sequential structure, and vice versa. Sequential and Parallel objects are treated as data which can be computed and manipulated before they are played. The result- arbitrary time structuring can be applied with much flexibility. As mentioned earlier, LOCO provides a base for composing using stochastic processes and free grammar context. Various effects can be produced, depending on how variables are defined, including; A random choice among its possible values A choice weighted by a probability distribution A random choice in which previous values cannot recur until all other values have been chosen Selection of a value in a fixed circular order The above are easily put together using composition (in a mathematical sense) of functions. For example, the value of an increment could be specified as a stochastic variable. This can produce a variable that performs a Brownian random walk. Brownian variables can be used, for example, as arguments in commands to instruments within a time-structured framework. These techniques can be used to construct concise, easy to read programs for transition nets and other stochastic processes. Using general programming language in each case, the operation of a program can be modified. See Ames (1989) for more information in the compositional uses of Markov chains. The primary design goals of LOCO include ease of use by non-programmers to experts. A more recent version of LOCO, LOCO-Sonnet mirrors LOCO but also includes a graphical front end. Sonnet is a domain independent data flow language originally designed for adding sound to user interfaces drawn from Jameson’s (1992) Sonnet. It is designed for use by both novices and experts alike. LOCO has been used in workshops for novices and professionals and even has courseware available. 4.3 Concluding comments on the Logo approach The Logo approach is known to be associated with constructivism. Constructivism, in the aspect of knowledge and learning, suggests that even in the cases where ‘objectively true knowledge, exists simply presenting it to a student limits the effects of their learning. It based on the assumption that learning arises from learners being interactive with the world, which will force them to construct their own knowledge. The result of this ‘knowledge’ will vary between individuals creating unique ideas and outcomes. This fits in very well with open-ended domains such as music where the basis of knowledge is learning how to create your ‘own’ masterpiece. Unlike classical Intelligent Tutoring Systems, Logo requires intensive support from a human teacher. This can be viewed as both weakness and strength of the program. Intelligent Tutoring Systems and the Logo approach were both influential ideas of AI in education in the early years. As both strengths and limitations were noted over the years, combining characteristics of the two became a prime focus of research which led to Interactive Learning Environments (ILE). We will talk about this after a brief discussion on AI-based tools. 5. Applications in Education: focus on AI-based tools There are a number of application tools employing AI but its purpose is not primarily educational. However, it is useful to consider some of these systems as they nevertheless have clear educational applications. There are quite a few programming languages based on AI languages such as LISP and CLOS that have a relatively similar technical aspect to that of the Music Logo systems described earlier. However, the philosophy of use may be quite different. The commercial system Symbolic Composer (for Macintosh and Atari) is one example of this difference. It has a vast library of functions, including neural nets facilities, used for processing, generating and transforming musical data and processes, commonly built on Lisp. The system is primarily aimed at composers and researchers. Another culture which offers an educational paradigm with many links to AI culture is the Smalltalk culture. An example of such a system is Pachet’s (1994) MusES environment, implemented in Smalltalk 80. It is aimed at experimenting with knowledge representation techniques in tonal music. MuSES includes systems for harmonisation, analysis and improvisation. Finally, an example of a commercial program is Band in a Box (Binary Designs, 1996). It takes a chord sequence as input and at output can play an accompaniment based on the chord in a wide variety of styles. At one moment in time this would have required AI techniques but in today’s era it is a conventional method. 6. Supporting learning with Computational Models of Creativity 6.1 A cognitive support framework: constraint-based model of creativity â€Å"I noticed that the [drawing] teacher didn’t tell people much†¦.Instead, he tried to inspire us to experiment with new approaches. I thought of how we teach physics: we have so many techniques-so many mathematical methods – that we never stop telling the students how to do things. On the other hand, the drawing teacher is afraid to teach you anything. If your lines are very heavy, the teacher can’t say â€Å"your lines are too heavy† because some artist has figured out a way of making great pictures using heavy lines. The teacher doesn’t want to push you in some particular direction. So the drawing teacher has this problem of communicating how to draw by osmosis and not by instruction, while the physics teacher has the problem of always teaching techniques, rather than spirit of how to go about solving physical problem† Feynman (1986) â€Å"John and I†¦.were quite happy to nick things off people, because†¦you start off with the nicked piece and it gets into a the song†¦and when you’ve put it all together†¦of course it does make something original† Paul McCartney quoted in (Moore, 1992) There are limitations present in both traditional AI approaches in education mentioned earlier (ITS and Logo). ITS’s don not work very well in problem-seeking domains and Logo type approaches require support from a human teacher in order to be effective. One way of investigating these problems has been addressed by MC (Holland, 1989, 1991; Holland and Elsom-Cook, 1990). ‘MC’ is an acronym for both ‘Meta Constraints’ and ‘Master of Ceremonies’, which is a general framework for interactive learning environments in open-ended domains. We will focus on the domain model rather than the teaching model. The current version is designed at teaching ab initio students to compose tonal chord sequences, with partic

Performance Appraisal Interview

Conducting the Performance Appraisal Interview The performance Appraisal interview offers an opportunity to discuss and compare perceptions of an employee’s job performance. Through open communication a supervisory and employee can assess job performance, measure actual result against expected results and plan for the future. The interview should not be used as a vehicle to bombard the employee or the supervisor with criticism, failures, faults, and / or errors. a) Opening the interview: The climate of the interview is essential to its outcome. The supervisor should set a tone for the interview that exhibits openness and support. Once this tone has been set, an employee will be more likely to share assessments of his or her performance, discuss strengths and weaknesses and commit to the development plans that are set in the interview. To set the tone, a supervisor should: 1. Review the purpose of the meeting. Clarify any questions the employee has and reaffirm that the interview serves to promote employee development through identifying job responsibilities, reviewing performance roles, Overall rating of performance and preparing a plan for improved or enhanced performance. 2. Regard the employee as an individual. Special concerns should be given to the employee’s communication style, new assignments, increased job responsibilities and performance standards. These considerations should guide the supervisor as he/she deals with the employee. b) Discussing performance. The second component of a performance interview is the actual discussion of an employee’s job performance. Recommendations for an effective discussion include: 1. Come prepared. Both the Supervisor should prepare objectives ahead of time and time and be able to cite specific example support observations and recommendations. . The employee should present his / her self – assessment first. The employee’s objectives are to present information regarding his or her job performance, pointing out strengths, and seeking assistance in areas where problems exist. This promotes openness and provides insight on how the employee view his or her responsibilities and performance 3. The supervisor should present his or her assessment of the employee performance after hearing the employee’s assessment. Areas of agreements should be discussed first, followed by areas of disagreement. Finally, any pertinent topics that were not brought up by the employee should be mentioned. The supervisor’s objective is to help the employee improve performance or develop skills to become a more productive employee. 4. Communication should be two – way. A dialogue should occur between the employee and the supervisor, with neither participant dominating the discussion. 5. Seek agreement on each point. If opinions differ when discussing individual responsibilities, performance roles or ratings, both the employee and the supervisor should express their ideas. Again, focus on behaviors relevant to performance. 6. Setting Training and development goals. Employee should be prepared to state his or her future plans for development. Discuss these plans realistically and set up appropriate goals and time tables. Supervisors should feel comfortable adding or suggesting development goals with the approval of the employee C) Closing the Interview. An important aspect of the entire process is how the interview ends. The following actions should be included in the closing portion of the interview. Summarize what has been discussed and agreed upon, making sure of consensus on all – important points. Do this positively and enthusiastically. †¢ Give the employee an invitation to react, question and share additional ideas and suggestions. †¢ Make arrangements to follow upon specific points if needed †¢ Set a date for the next performance review session. ( -Semi annual or quarterly ) †¢ Thank Each other for the time and energy that went into the review and end the interview on a positive or encouraging note. †¢ Complete disseminate the Appraisal Form as earlier. Avoid these Mistakes when Completing a Performance Appraisal 1. Recency Too much focus on the most recent examples of behavior rather than considering overall performance. This can occur because of inadequate record keeping. 2. Central Tendency Managers tend to rate every one about the same, or ate last, they avoid extreme ratings. The reviewer should use the ends of the scale as well as the middle. 3. Leniency Managers shun low rating to avoid conflict or because they believe that low ratings reflect badly on the reviewer. This can happen when the reviewer is rushed or under pressure to complete the Appraisal 4. Horns / Hallow Effect A tendency to rate the same individual â€Å"Excellent† on every trait or â€Å"Unsatisfactory† on every trait. This may happen when the supervisor feels that the employee has some shortcomings and then rates them poorly on everything as a result (or conversely rates too high on everything based upon a few high ratings). 5. Constancy Some Managers rate their employees in rank – order rather than on an individual basis and adjust scores to match the ranking order. . Similarity A tendency to rate employees, who have similar values and interest to the reviewer, hire. Additional Factors Affecting Performance Appraisal Ratings: †¢ Length of service and the â€Å"compliancy† of the person being rated can affect ratings significantly. †¢ Previous review ratings influence current reviews, whether the current manager or a former one did the pre vious review. †¢ Supervisors â€Å"guess† when they aren’t sure or don’t have a lot of experience with a given employees behavior.

King Edward II of England

This profile of King Edward II of England is part ofWhos Who in Medieval History Edward II was also known as: Edward of Caernarvon Edward II was known for: His extreme unpopularity and his general ineffectiveness as king. Edward lavished gifts and privileges on his favorites, fought against his barons, and was ultimately overthrown by his wife and her lover. Edward of Caernarvon was also  the first Crown Prince of England to be given the title Prince of Wales. Occupations: King Places of Residence and Influence: Great Britain Important Dates: Born:  April 25, 1284Crowned:  July 7, 1307Died:  September, 1327 About Edward II: Edward appears to have had a rocky relationship with his father, Edward I; upon the older mans death, the first thing the younger Edward did as king was give the most prestigious offices to Edward Is most notable opponents. This did not sit well with the late kings loyal retainers. The young king angered the barons still further by giving the earldom of Cornwall to his favorite, Piers Gaveston. The title Earl of Cornwall was one that had hitherto only been used by royalty, and Gaveston (who may have been Edwards lover), was considered foolish and irresponsible. So incensed were the barons over Gavestons status that they drew up a document known as the Ordinances, which not only demanded the favorites banishment but restricted the kings authority in finances and appointments. Edward seemed to go along with the Ordinances, sending Gaveston away; but it wasnt long before he allowed him to return. Edward didnt know who he was dealing with. The barons captured Gaveston and executed him in June of 1312.   Now Edward faced a threat from Robert the Bruce, the king of Scotland, who, in an attempt to throw off the control England had gained over his country under Edward I, had been retaking Scottish territory since before the old kings death. In 1314, Edward led an army into Scotland, but at the Battle of Bannockburn in June he was roundly defeated by Robert, and Scotlands independence was secured. This failure on Edwards part left him vulnerable to the barons, and his cousin, Thomas of Lancaster, led a group of them against the king. Beginning in 1315, Lancaster held real control over the kingdom. Edward was too weak (or, some said, too indolent) to dislodge Lancaster who was, unfortunately, an incompetent leader himself, and this sad state of affairs persisted until the 1320s. At that time the king became close friends with Hugh le Despenser and his son (also named Hugh). When the younger Hugh attempted to acquire territory in Wales, Lancaster banished him; and so Edward gathered some military might on behalf of the Despensers. At Boroughbridge, Yorkshire, in March of 1322, Edward succeeded in defeating Lancaster, a feat that may have been made possible by a falling-out among the latters supporters. After executing Lancaster, Edward annulled the Ordinances and exiled some of the barons, freeing himself from baronial control. But his tendency to favor certain of his subjects worked against him once more. Edwards partiality toward the Despensers alienated his wife, Isabella. When Edward sent her on a diplomatic mission to Paris, she began an open relationship with Roger Mortimer, one of the barons Edward had exiled. Together, Isabella and Mortimer invaded England in September of 1326, executed the Despensers, and deposed Edward. His son succeeded him as Edward III. Tradition has it that Edward died in September, 1327, and that he was probably murdered. For some time a story circulated that the method of his execution involved a hot poker and his nether regions. However, this gruesome detail has no contemporary source and appears to be a later fabrication. In fact, there is even a recent theory that Edward escaped his imprisonment in England and survived until 1330. No consensus has yet been reached on the actual date or manner of Edwards demise. More Edward II Resources: Edward II in Print The links below will take you to an online bookstore, where you can find more information about the book to help  you get it from your local library. This is provided as a convenience to you; neither Melissa Snell nor About is responsible for any purchases you make through these links.   Edward II: The Unconventional Kingby Kathryn Warner; with a foreword by Ian MortimerKing Edward II: His Life, His Reign, and Its Aftermath 1284-1330by Roy Martin Haines Edward II on the Web Edward II (1307-27 AD)Concise, informative bio at Britannia Internet Magazine.Edward II (1284 - 1327)Brief overview from BBC History. Medieval Renaissance Monarchs of EnglandMedieval Britain   The text of this document is copyright  ©2015-2016 Melissa Snell. You may download or print this document for personal or school use, as long as the URL below is included.  Permission is   not  granted to reproduce this document on another website. For publication permission,  please   contact  Melissa Snell. The URL for this document is:http://historymedren.about.com/od/ewho/fl/Edward-II.htm

Workplace Stress Among The Employee - 951 Words

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