1 What does the acronym SOLVER stand for 2 Based on your ans

1. What does the acronym SOLVER stand for?

2. Based on your answer in #2 above, how does this apply in problem solving.

3. Well-designed word problems integrate a variety of interrelated concepts and skills. Select a word problem from any high school math textbook and determine whether this problem is well designed and what concepts and skills are integrated in this type of word problem.

Solution

1.S: solve the problem
O: outline the options
L: limit and apply the solution
V: verify the solution
E: explain the results

R:

2.whenever a solver solves the problem,he must see what are the choices to solve the problem ie options which are given in the problem and he think what are the formulas have to apply and finally he will apply to the problem to get the result. after that he verifies the solution from the options and expalin the solution how he got it.

3.in an important paper, Mayer (1981) established a framework for classifying algebra

word problems from ten standard algebra textbooks used in California secondary schools.

Based on the underlying source formulae (e.g., rate × time = distance), he classified these

problems into eight families which could then be subdivided into problem categories consist-

ing of different templates. Mayer showed that learning to solve algebra word problems re-uires the acquisition of large amounts of domain-specific knowledge (Mayer, 1987). During

the solution process, both mathematical and real-world knowledge retrieved from long-term

memory have to be combined with the problem context in working memory in order to de-

velop a solution plan (Koedinger & Nathan, 2004).Further,

large pools of items can easily be designed and need not necessarily be calibrated in order to

assess a subject’s ability (Embretson, 1999). An IRT model that is suitable for analyzing the

difficulty of cognitive processes in rule-based

item is the LLTM.

One well-designed study using algebra word problems was conducted by Enright and

Sheehan (2002). They demonstrated three us

eful dimensions for understanding performance

differences in solving algebra word problems: mathematical complexity, context, and \"alge-

braicness\". In this study, complexity referred to characteristics like number of operations,

number of constraints and number of levels of

parentheses. Context was varied by features

like DRT (rate × time = distance), cost per unit or probability. \"Algebraicness\" referred to

the possible manipulation of variables (i.e., ”T-Shirts that usually cost $8.00 per case are on

sale for $6.00 per case. How many cases can J

ohn buy on sale for the price he usually pays

for x cases?”). IRT and regression analyses

showed that DRT items which required using

variables were more difficult than those which did not. Among the items without operations

on variables, the items with a cost context were significantly easier than the items with a

DRT context