個別指導 Smart,Grid

Schlechte Noten? Demotiviert im Unterricht? – Gehirngerecht lernen und mit Freude zum nachhaltigen Erfolg!

Hat Ihr Kind schlechte Noten, weil ihm die Grundlagen in bestimmten Fächern fehlen?
Muss Ihr Kind sich fundiert auf eine wichtige Prüfung vorbereiten?
Hat Ihr Kind den Spaß am Lernen verloren?

Sie wollen, dass Ihr Kind mit Leichtigkeit und Freude bessere Noten erzielt?
Sie wollen, dass Ihr Kind fehlenden Unterrichtsstoff schnell nachholt - ohne stressiges Pauken und ohne Überforderung?
Sie wollen Ihr Kind wieder motiviert in den Unterricht gehen sehen?

Das Erreichen wir gemeinsam in meinem Unterricht - effizient und gehirngerecht!!!

1) Damit Fortschritte sofort sichtbar werden,
Gerne widme ich mich Ihrem Kind ganz individuell,

- kostenloses gründliches Vorbereitungsgespräch (45 Min.),
In die Vorbereitung meiner Unterrichtsstunden fließen das Lerntempo, der Wissensstand sowie die individuellen Bedürfnisse Ihres Kindes und Gegebenheiten Ihrer familiären Situation ein.

- Nach Ihren Wünschen werden die Themen behandelt, die aktuell Schwierigkeiten bereiten. Der Stoff wird erklärt und Übungen gemeinsam gelöst. Hierbei wird auch das jeweils benutzte Schulbuch einbezogen, damit Fortschritte im Schulunterricht sofort sichtbar werden.

- Haben sich große Wissenslücken in den Grundlagen angehäuft?
Dann kann Ihr Kind auch gerne einen „Von-Anfang-an-Kurs“ erhalten. Hier kann es das gewünschte Fach von Grund auf in seinem ganz individuellen Tempo erlernen – neben dem Schulunterricht oder in den Ferien.


2) Damit Gelerntes behalten wird,

Erleichtern Sie Ihrem Kind das Lernen mit neuen Methoden,
In meinen Unterricht fließen verschiedene gehirngerechte Lern- und Arbeitsmethoden mit ein, z.B. die Birkenbihl-Methode, Pomodoro-Technik, WOOP, Pareto-Prinzip u.v.m. Natürlich passe ich mich aber auch gerne der gewünschten Unterrichtsmethode an.

3) Damit Sie Ihr Kind bestmöglich unterstützen können,

- Seien Sie stets auf dem neuesten Stand!
Als erfahrene mehrfache Mutter lege ich Wert auf einen regelmäßigen Austausch mit den Eltern meiner Schüler. Sie und Ihr Kind erhalten nach der Stunde stets eine kurze Rückmeldung.

- Behalten Sie stets den Überblick!
Mein Unterricht ist klar strukturiert. Am Anfang erstelle ich mit Ihnen und Ihrem Kind gemeinsam einen Lernplan, der nach der Smart-Methode spezifische, messbare, akzeptierte, realistische und terminierte Lernziele enthält.

- Elterngespräche sind nach Absprache gerne willkommen.

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I AM VERY EXPERIENCED TUTOR AND MOST OF THE STUDENTS ARE GETTING 90 + MARKS..

I am 27 years old, I am extremely Smart, patient, great with children. I used to be a teacher and children truly adore me as much as I do them. I treat them as the individuals they are, and tailor their tutoring to their unique learning stytles (what works best for them) My main technique is to bring the fun back into learning. I guarantee your child will not only bring home better grades, but enjoy doing so.

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my hourly charges in HK$350 - 400 per hour (negotiable)

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Relevant Units covered in Undergraduate Studies.• Applied Mathematics-1( Complex Variables, Vector Algebra, Calculus Taylors theorem, expansion of functions
in power series, partial derivatives of first and higher orders, total differentiation concept of commutative partial derivatives, Eulers theorems of homogeneous functions, deduction from Euler’s theorems ,errors, approximations, maxima and minima functions of two variables.)
• Applied Mathematics-2( Exact differential Equations, Linear equations & reducible to linear (Bernoulli equations), Linear Diff. Eqn. of nth order with constant coefficients, complimentary function & particular integral when the function of the
integral on the R.H.S. are exponential, Sin(ax + b), Cos(ax + b).Cauchys Linear equation( Homogenous eqn.). The Legendre Linear equation, Variation of parameters & method of undetermined coefficients. Elementary application of above diff. Eqn. in solving engineering problems from Electrical Engg., Chemical Engg., Mechanical Engg., and Civil Engg. Integral Calculus: Rectification of plane curves, Double and Triple integrals, Their geometrical interpretation & evaluation. Evaluation of double integrals by change of order and change to polar. Application of double and triple integrals to areas, volumes & mass. Beta & Gamma Functions.)

• Applied Mathematics 3(Fourier Series and Integrals: Orthogonal and orthonormal functions, expression of a function in a series of orthogonal functions,s ine and cosine functions and their orthogonality properties. Fourier series, Drichlet conditions, periodic functions, even and odd functions, half range sine and cosine series, Parseval's relation. Complex form of Fourier series, introduction to Fourier integral, relation with Laplace transform. Laplace Transforms: Function of bounded variable ( statement only ), Laplace transforms of 1, at, exp( at ), sin( at ), cos( at ),sinh(at), cosh(at), erf(t), shifting properties, expressions with proofs for L { t f(t) }, L { f(t)/t }, Laplace of an integral and derivative)

• Applied Mathematics 4(Complex Variables: Regions and paths in the Z plane. Path/Line integral of a function. Inequality conditions for a path integral to be independent of the path joining two points. Contour Integral, Cauchy's theorem for analytical functions with continuous derivatives. Matrices: Brief revision of vectors over real field, inner product, normal, linear independence, orthogonality. Characteristic values and vectors, and their properties for Hermitian and real Symmetric matrices. Vector Calculus: Scalar and Vector point functions, directional derivative, level surfaces, gradient, surface and volume integrals, definition of curl, divergence. Use of operator. Conservative, irrotational, solenoidal fields. Green's theorem for plane regions and properties of line integral in a plane.)

• Applied Mathematics 5(Probability and topics in Statistics: Statistical experiments with random outcomes, Sample space, probability defined on the basis of sample space and on the basis of events and their combinations. Theorem on probabilities, conditional probability. Bayes theorem. Random variable, probability distribution for discrete and continuous random variables. Density function and distribution functions. Expected values, variance , moments, moment generating functions, Bernoulli's trials, Binomial , Poisson, normal distributions for detailed study with proof, Other common distributions, T , F, Beta, Gamma, X with indication of the applications, Central limit theorem, Bivariate probability and frequency distributions, Correlations, regression, lines of regression. Introduction to random samples, use of random numbers, stochastic processes, Time series , queuing theory. Optimization Techniques- Problem formulation, Simplex Method, Revised Simplex Method, Duality & Sensitivity. Unconstrained optimization of several variables• Numerical methods for unconstrained optimisation : Random search & Univariate method, Fletcher Reverse method, Newtons method.)
• Discrete Mathematics ( Logic : Propositions and logical operations, Truth tables, Equivalence and implication, Laws of logic, Mathematical induction and quantifiers. Set theory : Method of proof for set, Venn diagram, set membership tables, definitions, Laws of set theory, Partition of sets. Permutations, combinations and discrete probability. Introduction to permutations and combinations, Generation of permutation and combination, Discrete probability, Conditional probability. Relations and diagraphs., Paths and the relations and diagraphs, Properties of relations, Equivalence relations, Computer representation of relations and diagraphs, Manipulation of relations, Transitive closure, Warshall’s algorithm.Function and pigeon hole principle Definition, Types of functions: injective, surjective, bijective, Composition, identity and inverse, Pigeon hole principle.Graphs , Posets, Hasse Diagram, Lattices, Finite Boolean Algebra, Groups & their Applications Introduction to Rings & Fields.)

Units covered in Postgraduate Studies.
• Advance Financial Mathematics (Access Grid Room -University of Wollongong): Brownian motion, Black-Scholes equation for pricing Digital options and Power options, Reflection principle and barrier options, Pricing options using Monte Carlo Simulations, Monte Carlo estimation methods for hedge ratio, Finite-difference methods for Vanilla options and Asian Options, C++ Programming.
• Financial Econometrics 2 (Monash University):Modeling asset return volatility, volatility modeling for measuring risk and pricing derivatives, continuous time stochastic Processes for pricing financial Derivatives, High Frequency data Analysis, Generalized Method of Moments in Financial Models.
• COMPUTATION IN Stochastics (Monash University): Stochastic differential equations, Taylor expansion of stochastic differential equations, Evaluation of option values. European option. American option, Optimization methods using C++.
• STOCHASTIC CALCULUS AND MATHEMATICAL FINANCE (Dr. Fima Klebaner- Monash University): Ito integrals and Ito’s formula. Stochastic Differential Equations and Diffusions, Calculation of expectations and PDE’s, Feynman-Kac formula. Martingales and Semi martingales. Change of Probability Measure and Girsanov Theorem. Fundamental Theorems of Asset Pricing. Change of Numeraire. Application to options.
• Stochastic Processes II - Random Walks & Markov Chains (Monash University): Simple Random Walks Discrete-time martingales. Markov chains, both continuous and discrete time.
• Applied Statistics: Sample Survey, Clustering, Classification, Principal Component Analysis and Time Series Analysis. (79/100).
• Game Theory and Applications (RMIT University): Strategic Form of Games, Incomplete Information, Cooperative Games.
• Nonparametric Curve Estimation (AMSI - Dr. Aurore Delaigle-University of Melbourne): Kernal Density Estimation, kernel Regression, Spline Regression, Wavelet Analysis and Bootstrapping.
• Financial Time Series (Access Grid Room- University of South Australia): Spectral decomposition, Box-Jenkins models, Forecasting techniques, Smoothing of time series, GARCH and other volatility models, Stochastic Differential Equations.
• Statistical Inference: Statistical Inference at the level of Lee Bain and Max Engelhardt (2000).

I have returned to India to settle from Cambridge UK. I am a very creative tutor and make my lessons very interesting and make the student work Smart.I have several years of taching students in the UK including my own children who too Public School Educatioin in Cambridge UK