INTRODUCTIONQuality education is key in the development of any nation. It provides opportunities for people to develop their competencies that can allow them to perform well in society and it enhances the social, intellectual and economic growth of communities (Openheimer, 2010). Education must bring the possibility to face continuous changes and professional competitiveness. Learning is a multifactorial phenomenon that requires attention as it is dependent on many social, political and psychological elements. Many studies related to factors attributed to students´ characteristics and the effect of these on their learning process, some of the factors more mentioned are: prior knowledge (Alexander & Judy, 1988; Ausubel, Novak, & Hanesian, 2006; Dochy, 1991a; Dochy & Alexander, 1995; Hailikari, 2009; Hattie, 2009; Marzano, 2004; Meltzer 2002; Roschelle, 1995; Sagastizabal, Perlo, Pivetta & San Martín , 2009; Shapiro, 2004; Thompson & Zamboanga, 2004), social factors (Alexander & Judy, 1988; Dochy, 1991a; Tinto, 1992), cultural factors (Sagastizabal et al., 2009), individual attributes like gender and race (Hattie, 2009; Tinto, 1992), interests and intentions from student (Hattie, 2009), commitment (Hattie, 2009; Tinto, 1992), motivation (Alexander & Judy, 1988; Boiché, Sarrazin, Grouzet, Pelletier & Chanal, 2008; Garris, Ahlers, & Driskell, 2002; Graham & Weiner, 1996; Hattie,2009; Wong, 2012), economic factors (Sagastizabal et al., 2009; Tinto, 1992), self-efficacy (Bandura, 1971, 1982, 2006; McKenzie & Schweitzer, 2001; Zimmerman, 1989), stress and anxiety (Hattie, 2009), communication and social skills (Porter, 2008), personality (Porter, 2008), emotions (Kleres, 2010), time spent to study (Grouws & Cebulla, 2000), learning approaches from student (López, Esteban & Pérez, 2006), metacognition and prior academic development (Dochy, 1991b), and more of them that could be listed.The Coleman report (1966) analyses education in public institutions in the USA through a qualitative study, including types of teachers, students, principals, and he says that the most important factor influencing academic achievement is what students bring to school with them such as prior education, family background, culture and interests (Coleman, Campell Hobson, McPartland, Mood, Weinfeld & York, 1966 ). They conclude that the majority of the contribution in the variance for academic development in students comes from those personal features.Thus learning should be significant and should be studied as a process that includes the interaction of the new knowledge to be learned and the knowledge subjects already posses. This research takes learning as a process of active construction of knowledge (Ausubel, Novak, & Hanesian, 2006). Among the problems observed in education we have low grades, high failure rates and high dropout indices, large groups when students´ have inconsistencies in their prior knowledge (Fernández, Mena & Riviere, 2010; González, Castañeda & Maytorena, 2000; Palacios & Andrade, 2007; Stinebrickner & Stinebrickner, 2013; Tinto, 1992). Ibáñez (2007) for example, suggests that the main causes of low school achievement are: missing study habits, low motivation from students, bad teaching practices, incorrect politics and unfit education models. In this chapter, the authors take the perspective of learning as a process, the relation to change from knowledge existing in the memory interacting with the new knowledge to be acquired. Information processing theory considers prior knowledge as an assimilation environment and states that new material is related and integrated to it (Dochy, 1991a). Ausubel (2006) when discussing assimilation theory maintains that acquiring new information depends mainly from the preexisting ideas in the cognitive structure, and that meaningful learning would be determined by those ideas. Thus it is important to study prior knowledge as a determinant of new learning (Alexander & Judy, 1988; Ausubel, Novak, & Hanesian, 2006; Dochy, 1991a; Dochy & Alexander, 1995; Hailikari, 2009; Hattie, 2009; Karabel & Halsey, 1977; Manzano, 2004; Thompson & Zamboanga, 2004). Hailikari (2009) established that one of the first researchers who affirmed the relevance of prior knowledge on the learning process was Bloom in 1956, when he wrote that the learning process is determined mainly by the cognitive behaviors or prerequisites, term used referring to prior knowledge.

Bloom also mentioned that more than a half of the variance of learning outcomes depends from prior knowledge.In the same direction, Ausubel, Novak y Hanesian (2006) pointed that if they had to reduce education to a sentence, the main factor influencing learning, is what the learner already knows, and that professors must consider this to teach consequently. Dochy (1991a) concluded that this sentence assumes three ideas: prior knowledge is an important variable in education; content and structure of prior knowledge must be relevant to new knowledge to obtain optimum results; and the better condition of learning is obtained in base of the accordance and connection to prior knowledge. Moreover it is important to define what we consider prior knowledge to be and what it isn’t, its different types, effects, nature and its inherent qualities in order to better understand how new knowledge gets integrated in to our cognitive structure and how it can be measured. The purpose of this project then was to develop a valid and reliable instrument that measures the specific inherent qualities selected from prior knowledge so later in an extended project we can attempt to determine the effect these characteristics have on learning.

PRIOR KNOWLEDGEThe main idea underlying the interaction between prior knowledge and new learning is not that prior knowledge itself leads to success or failure in learning. Instead, prior knowledge is conceived as the raw material that conditions learning. Furió and Guisasola (2001) point out that one of the most worrying aspects of prior ideas is not that they are correct or incorrect but rather the persistence of information. Larkin (1983) mentions that prior knowledge is concrete, individual, complex and it is resistance to change. Moreover Emeigh (2008) pointed out that the more complex the new concept to learn is, the greater complex cognitive structure required.Prior knowledge is present before the implementation of any instruction or learning task, and has some proprieties such as availability, accessibility, recovery, dynamism, transferability, quantity, with structured schemes and with levels of relevance to the new learning task (Dochy & Alexander, 1995; Dochy, Moerkerke & Segers, 1999). When prior knowledge is characterized as being dynamic, it means that knowledge changes according to the passage of time and as proposed by Arroyo, Morales, Silva, Camacho, Canales and Carpio (2008) this knowledge is not controllable in a direct manner.Furthermore, according to Hertwig and Todd (2003), the database where we obtain our inferences and predictions has limited amount and limited processing memory, also called working memory. Hernández (2012) adds that the capacity of the processing memory is limited in duration and in units of processed information called chunks or bits.

PRIOR KNOWLEDGE CLASSIFICATIONMany classifications have emerged to describe types of prior knowledge, we can establish some of the most important considered for this research. Alexander, Schallert and Hare (1991) employed one classification of subcategories in terms of the specialization of the body of knowledge, which could be considered areas or branches of information. They propose categories such as the content knowledge that refers to concepts from some general area of knowledge (e.g. scientific knowledge); discipline knowledge that is used for highly subsets of a specialized field of study (e.g. physics); and domain knowledge, mainly used to refer to subjects (e.g. electricity and magnetism or calculus). In this classification, the most general name of prior knowledge could be the content knowledge and the most specific could be the domain knowledge.There are other classifications proposed by Dochy and Alexander (1995). They mention different states of knowledge as procedural knowledge which is related to steps, plans, rules, skills and operations, also called as know how. Declarative knowledge, on the other hand is concerned with concepts, symbols, meanings and definitions, also called as know that. Finally conditional knowledge refers to the situation of the context of learning and is also known by other authors as know when and where.

Prior knowledge has a broader typology when it comes to its inherent qualities (Dochy, 1991a). Prior knowledge inherent qualities are characteristics of the different information stored in the memory. An important classification of these inherent qualities is done by Ambrose, Bridges, DiPietro, Lovett & Norman (2010), which includes: content, quantity and structure. The characteristics of content are related to the comparison of the information from the student with the knowledge defined by the science, two inherent qualities are possible: correctness or misconceptions. From the subset of quantity, another three inherent qualities are possible: completeness, incompleteness or absence. Finally, from the structure subset which consists of hierarchies of concept, relation and meta-relation types (Nguyen, Kaneiwa & Nguyen, 2010) and that it is possible to have another two inherent qualities: wrong structure or correct structure. From the inherent qualities already mentioned, the characteristics selected in this study are from the subsets of content and quantity (see table 1).

Table1 Prior knowledge inherent qualities selected for the study

Subset Inherent Qualities Condition of prior knowledge Completeness Enough for the learning task

Quantity Incompleteness

Not enough & without mismatches

Absence No response Content Correctness Matches with science

Misconception Mismatches with science

The inherent qualities on the table 1 for content are completeness which represents enough information to succeed learning the new knowledge, incompleteness which is related to some information missing and to the amount of information present (some correct but not complete) and absence that represents no prior knowledge exposed by the student; for the quantity subset there can be misconceptions when the information is incongruent with what is accepted by society or science and correctness which is the characteristic where the prior knowledge matches with the information accepted by the science. In addition, as it was said in the description of knowledge, the prior knowledge has other inherent qualities that are not considered in this study such as availability, accessibility, durability and relevance.Another classification of prior knowledge is depending on its nature that can be observable or not (Dochy & Alexander, 1995; Reber, 1989). It can be explicit for knowledge that can be observed or measured (e.g. when a student state the response of a question in a test), and can be tacit when knowledge cannot be clearly observed or identified (e.g. the process that a student used to get to the explicit answer that is not expressed in).Finally one important classification done by Ambrose, Bridges, DiPietro, Lovett, and Norman, (2010) is related to the effects that prior knowledge could have with the new information to be learned. The effects mentioned by them are the ones that facilitate and contribute in the learning process commonly referring to completeness and correctness, while the others are the ones that interfere in the acquisition of new information like misconceptions, and it is not very clear which is the effect of absence and incompleteness of prior knowledge.There are three types of learning depending on the conditions of the inherent qualities of prior knowledge. The first condition is when the student doesn´t have any prior knowledge related to the specific topic to be learned. The process of learning involved with the absence of prior knowledge consists in the acquisition of new knowledge. The second condition is when students have some knowledge contents that are correct, but not complete. This kind of learning involves a gap filling process (Carey, 1999). The third condition is when prior knowledge has conflicting information with

the new knowledge to be learned, or when prior knowledge has misconceptions. This process of learning requires a conceptual change (Chi, 2008).

PRIOR KNOWLEDGE ASSESSMENTNow that we presented some prior knowledge categorizations and some of the learning process involved, we can turn to the design of a test to measure students´ prior knowledge. Due to the many types of knowledge described, it is necessary to select the types that are relevant or at least measurable. An explanation and justification of the inherent qualities already selected is required to clarify the study. First, a distinction of procedural knowledge and declarative knowledge has to be separated in different tests to identify the distinction of processes and concepts. These was identified in a prior research (Arellano, Mendoza & Villarreal, 2015) when a test was developed to detect the inherent qualities of the knowledge involved to solve problems for electrostatics specifically on Coulomb law on engineering students. The test had the responses already included but with some errors intentionally included, asking to the students´ to identify them. Issues emerged because of the design of the instrument that didn´t establish clearly the responses that had different inherent qualities of between declarative and procedural prior knowledge. Second, the test posed some problems to measure procedural knowledge because the items were sequenced as series and if any of the first items was answered incorrectly by the students, the later items couldn´t be answered. For these reasons, the procedural knowledge had to be set aside in this part of research and only declarative inherent qualities of the prior knowledge were considered for measurement. Similar to this problem from measuring procedural prior knowledge, the inherent qualities from the subset of structure define relations between prior knowledge concepts and operations in hierarchy, something difficult to do because of the design of the multiple choice test selected.In order to assess declarative prior knowledge present in the memory of each student, we selected the inherent qualities of correctness, completeness, misconceptions, incompleteness, and the absence of knowledge (figure 1). The definitions of each inherent quality of prior knowledge selected used for the design of the instrument are:Correctness is related to the consistence, acceptance and congruence of the student knowledge with the scientific knowledge (Ambrose, Bridges, DiPietro, Lovett & Norman, 2010). Completeness is related to a very close enough amount of information of declarative knowledge from the student compared to what is defined by science. It is possible to identify that the definition of correctness and completeness are very similar because they have to be equal or almost equal to what is scientifically accepted, but correctness is related to the content of the prior knowledge and completeness refers to the quantity of prior knowledge. There are coincidences and differences that are easy to detect in the conceptual definition of this two inherent qualities, but when it is time to measure them it is hard to do it by separate or mutually excluding. For measuring purposes on the test, to consider a prior knowledge as a correct definition from the student, the information must match in quantity and content with science definitions. An example of correct prior knowledge is when a student affirm that the unit of linear length in the International System of Units is the meter, because that is the scientific definition.

A Misconception is incorrect knowledge or the presence of errors in students´ knowledge, when the information is incongruent with what is accepted by the society or science (Ambrose, Bridges, DiPietro, Lovett & Norman, 2010; Smith, diSessa & Roschelle, 1993; Taylor & Kowalski, 2004). Some of the terms used to name the misconceptions are: distorded understanding, inaccurate prior knowledge (Ambrose, Bridges, DiPietro, Lovett & Norman, 2010), alternative conceptions (Hewson & Hewson, 1983), naive beliefs (Caramazza, McCloskey & Green, 1981), alternative beliefs (Hammer, 1996), false beliefs (Chi, 2008; Taylor & Kowalski, 2004), false conceptions (Duit, 1993), incorrect beliefs, misinformation, inaccurate belief, inconsistencies (Taylor & Kowalski, 2004), incongruous beliefs (Rebich y Gautier, 2005), alternative frameworks (Taber, 2001), naive theories (McCloskey, 1983; Pine, Messer & John, 2001), erroneous ideas, mistaken conceptions, misunderstandings

(Thompson & Zamboanga, 2004), mistaken ideas, inadequate existing knowledge, faulty conceptions, flawed conceptualizations (Smith, diSessa & Roschelle, 1993) flawed mental models (Chi, 2008), misconceived qualitative explanations (Chi, Roscoe, Slotta, Roy & Chase, 2012). One very common example of misconception is when students´ think that earth is flat.

Incompleteness refers to some information correct and some information missing, in other words, the information is needed in its entirety as required for the new learning (Dochy, 1991a; Levesque, 1981). Incomplete knowledge has a deficit of information in quantity and complexity. Terms used to talk about knowledge that is not complete are: lack of information (Christen & Murphy, 1991; Taylor & Kowalski, 2004), critical gaps between new and prior knowledge, insufficient knowledge (Ambrose, Bridges, DiPietro, Lovett & Norman, 2010) and significant gap of knowledge (Ungar, 2000). An example of knowledge incomplete is when a student is asked for the definition of sine and cosine in a right triangle and he or she knows which of the sides are the legs and the hypotenuse, but doesn´t know the definition of sine and cosine.

Absence of knowledge is the information that is not available as a mental representation in students (Smithson, Bartos, & Takemura, 2000). Another terms used to refer to the absence of knowledge are: scientifically uninformed, analfabetismo científico or scientific illiteracy, ignorance (Risbey & O´Kane, 2011; Ungar, 2000), unknown or uncertainty (Moss & Schneider, 2000), effective ignorance (Risbey & Kandlikar, 2007). A case of the absence of knowledge is when the student affirm that doesn´t have any response.

Figure1. Inherent qualities of prior knowledge selected for this study adapted from Ambrose, Bridges, DiPietro, Lovett, & Norman, (2010).

PROBLEM STATEMENTSeveral studies have determined that prior knowledge affects the learning process (Alexander & Judy, 1988; Ausubel, Novak, & Hanesian, 2006; Dochy, 1991a; Dochy y Alexander, 1995; Dochy & Segers, 2014; Hailikari, 2009; Hattie, 2009; Marzano, 2004; Meltzer 2002; Roschelle, 1995, 2004; Sagastizabal, Perlo, Pivetta & San Martín , 2009; Shapiro, 2004; Thompson & Zamboanga, 2004), however there has not been research conducted in order to establish clearly how each inherent quality of prior knowledge affects differently the learning process. The qualities of prior knowledge more mentioned and with particular interest are those selected in Figure 1. The premise of this study is that we could assess the amount and content of prior knowledge inherent qualities then the learning process could be improved or at least better understood. One of the objectives we set up to achieve was the design of a test with a high Cronbach alpha coefficient that would provide some information regarding prior knowledge so later it could be correlated with learning outcomes.The revision of literature in the topic of prior knowledge showed that there is no instrument with the characteristics aimed at measuring the inherent qualities selected (Barniol & Zavala, 2013, 2014; Giglers & de Jong, 2005; Greca & Moreira, 1998; Hestenes, Wells & Swackhamer, 1992; Knight, 1995; Nguyen & Meltzer, 2003; Thornton, 2014). Each item designed was standardized and made available in a Moodle platform. In order to design the instrument, we selected a unit from the syllabus of electricity and magnetism which forms part of the curriculum of majors in engineering. Thus we selected concepts and definitions from this unit in the study of electrostatic that included among others the topic of the law of Coulomb.One of the main questions raised in the design of this instrument was: How can we assess prior knowledge´s inherent qualities? Considering the design of a mutually excluding of the inherent qualities in the instrument, we took as a starting point previous research conducted in which procedural knowledge turned out to be too complex because of the nature of this knowledge has a step related to

another step and it doesn´t allow us to create items with mutually excluding responses. Another important aspect for the design of the instrument is the way the information desired will be collected due to time cost and difficulties in the analysis of each student test. Also validity is a characteristic desired and required in the test, for that reason the review of instruments used previously was a better starting point in order to improve the quality and evaluation of the instrument.

METHODThe study took place at a higher education institute of technology located in Chihuahua, México. The participants in the study were students majoring in engineering who take, as part of their curriculum, the course of electricity and magnetism. Programs in engineering at this institute that have this course in their curriculum include electro mechanic, mechanic, electronic and electric engineering students. The participants were 36 students beginning their higher education program. This particular subject is taken during the first semesters of the program and it has been determined causes a high proportion of failing grades in students. The gender and age distribution of the sample consisted of 34 men and 2 women between 19 and 24 years old. This sample of students was chosen because they were students taking the subject at the moment of this study and because students from other majors (materials, chemical and industrial engineering) don´t take it as part of their curriculum with the same contents as the students selected in the sample. The students in this course are considered that they must have some prerequisite knowledge. However one of the objectives of this study was to be able to evaluate and classify this knowledge according to the inherent qualities defined above. Prerequisite knowledge is defined as prior knowledge that students need to successfully acquire new knowledge from the electricity and magnetism course and that it is considered fundamental in developing their new knowledge to solve problems related to electrostatics. Among this prerequisite knowledge in particular we examined Coulomb´s Law. This prerequisite knowledge was determined by two senior professors considered as experts in the subject of electricity and magnetism and who have taught this subject for over 20 years. The knowledge required to solve problems of Coulomb´s Law involves an understanding of systems of units, prefixes, Pythagoras theorem, sine and cosine definitions and vectors properties.

Instrument DesignThe instrument designed for this project was aimed at collecting information of students´ prior knowledge, the basis for its construction was taken from:1. Text-books from electricity and magnetism subject (Cheng, 1998; Zemansky, Young, & Sears, 2005).2. Handbooks for item elaboration (Carreño, 2001; Chávez, C., & Saade, A. 2010; Gallardo, 2010).3. Tests already validated (Barniol & Zavala, 2013; Barniol & Zavala, 2014; Hestenes, Wells & Swackhamer, 1992; Thornton, 2014).4. Knowledge from experienced teachers on electricity and magnetism considered to be experts who had at least 20 years of teaching the subject.

The design of the instrument consisted in 10 multiple choice items (see figure 2 for example) that consider the inherent qualities of prior knowledge, that is, incompleteness, correctness, misconceptions and the absence of knowledge (Arellano, Mendoza & Villarreal, 2015).

Figure 2. Example of one of the items in the instrument for measuring prior knowledge inherent qualities.

The instrument has the structure of two-tier form commonly used to measure students´ prior knowledge. Each question measures declarative knowledge established with propositions of multiple

choice, then each has a second follow up question aimed at identifying if students have a clear justification of their previous response (Tan, Goh, Chia & Treagust, 2002).For the operationalization of each inherent quality of prior knowledge, the value assigned to determine the presence of correctness was 5. For prior knowledge that is incomplete a 2.5 was assigned if a student selects options that have this inherent quality. In the same way for prior knowledge that is incorrect a -2.5 value was assigned. For absence of knowledge, the value assigned was 0. The option of other was valued with 0.5, to give the option to students if they didn´t find their response among the choices in the instrument. When students didn´t answer correctly the declarative knowledge question, the response of the second question (justification question) was assigned with the value of 0.25 because it won´t be acceptable to answer it if the prior question was answered incorrectly. Once the values were assigned to each inherent quality we could obtain the reliability of the instrument.

Validity of the InstrumentAn instrument that attempts to measure prior knowledge needed by students´ in a course of electricity and magnetism should have content validity. Kerlinger and Lee (2002) define content validity as the value judgments from experts or critic evaluation included in each test item. The content validity is the representativeness or the sampling adequacy in an instrument. The researcher that designs the instrument also defines the properties to measure. With this definition of content validity, we selected two full time expert professors on the subject of electricity of magnetism, with more than 20 years of experience in the subject. They were informed of the objective of this project and the design of the instrument so they could suggest some of the contents that have been most commonly detected to cause difficulties in students. The experts revised the content and structure of the instrument by reading it and suggested changes in the writing of the items.

Reliability of the instrumentReliability represents the stability, consistency and predictability of an instrument (Kerlinger & Lee, 2002). Reliability refers to the degree to which the measurement is consistent with itself. The test has some revisions from professors from the public higher education institute of Chihuahua experts on the subject. For this research the goal was to obtain an instrument with a Cronbach alpha coefficient higher than 0.7.

RESULTSOne of the main goals of this research was to create a reliable and valid instrument that could measure students´ prior knowledge. The instrument designed consisted of 20 items. Ten multiple choice questions had the objective to measure students´ declarative prior knowledge and the other ten multiple choice questions had the objective to verify the students’ justifications and to reduce responses from guessing. Each of the items has a choice of responses that corresponds to the prior knowledge inherent qualities measured: correctness (that includes completeness), incompleteness, misconceptions and absence of knowledge. The options of response “other” and “no response” were also included. Table 2, exemplifies the responses of the students.

Table2 Example of the responses of the students collected on a table introduced on SPSS program to measure Cronbach alpha coefficient

Student Question Number

1 Q1 Q2 Q3 Q4 Q5 Q6… … Q1

5 Q16 Q17 Q18Q19 Q20

2 2.5 0.25 5 5 5 5 - - 5 2.5 5 5 5 5

… 0 0.25 5 5 5 5 - - 5 5 5 5 5 5… 2.5 0.25 0.5 0.25 5 5 - - 5 5 5 5 5 -2.5… 5 2.5 5 5 5 5 - - 5 2.5 5 5 5 536 5 2.5 5 5 2.5 0.25 - - 0 0.25 0 0.25 0 0.25

The responses of the student were collected and processed using SPSS software. The analysis yielded a Cronbach alpha coefficient of 0.706, which gives the result of a reliable instrument that contains 20 items. None of the items were excluded from the analysis. In table 3 is showed each item with its standard deviations.

Table 3 Item standard deviations and corrected item total correlations

Question Standard deviation Corrected Item total correlation

1. Newton is the unit for: 1.44 -0.1212. Justify answer on question 1 1.96 0.1333. Mu prefix represents: 2.41 0.2834. Justify answer on question 3 2.26 0.295. 5cm equals how many meters: 0.7 0.3236. Justify answer on question 5 1.74 0.2487. Identify opposite leg on a triangle: 1.74 0.1118. Justify answer on question 7 2.0 0.159. Sine definition using triangle: 2.51 0.08810. Justify answer on question 9 1.79 0.17111. Pythagoras definition using a triangle 2.55 0.31812. Justify answer on question 11 2.3 0.28113. Vector properties are: 1.48 0.36414. Justify answer on question 13 1.47 0.32715. Components x and y on F vector are 2.63 0.22916. Justify answer on question 15 2.48 0.43817. Magnitude of a vector 1.74 0.56318. Justify answer on question 17 2.15 0.619. Unitary vector 2.64 0.57920. Justify answer on question 19 2.52 0.22

DISCUSSIONThere have been several attempts to identify the effects of prior knowledge on learning. There is no instrument to measure prior knowledge in electrostatics with the same characteristics the developed test has. Similar tests have been designed with the two-tier structure but they don´t differentiate prior knowledge inherent qualities. The results of the study showed a valid and reliable instrument with 20 items carefully designed. Each item has the capacity to mutually exclude the different inherent qualities of prior knowledge. The inherent qualities of prior knowledge selected on this study (correctness, incompleteness, incorrect and absence of knowledge) have been mentioned in many studies but these studies don’t attempt to measure it in the way proposed in this project. The instrument was considered reliable and takes into account different designs that had already been used. The two-

tier type of the instrument helps to reduce the random answers from the students by requiring them to justify their responses. For the present research, it was necessary to create and adapt an instrument that included the variables that were of interest for this instrument. Although one of the items had negative correlation for the total of the items, the acceptance of the instrument yielded overall good results that shed light in ways of determining and measuring prior knowledge in students.

SOLUTIONS AND RECOMMENDATIONSSuggestions could be presented when we finished this research. One of the most important recommendations is that the instrument must be enriched with more items. If another instrument is designed to measure prior knowledge it is important to define carefully the variables or the characteristics to be measured. The instrument must mutually exclude the variables in each multiple choice item. The design of the questions must take in account that students´ could guess the right answer, so it is important to get the more information as possible from the students´ asking them to justify their response. In order to reduce the guessing answer it could be useful to select the two-tier structure of an instrument. Some of the handbook to create instruments pointed out that each item must have the same extension of the responses included in their multiple choices, this means that every choice must be similar in the quantity of words or values presented for the student. Other problems identified with the design of items are related to the clarity of the sentences. It is also recommended to use the instrument already designed on this research to score students´ prior knowledge inherent qualities as a diagnosis. This also would help to verify if the instrument is adapted to identify prior knowledge inherent qualities from students´ with different characteristics and to increase the sample of the study. For the necessity to identify the inherent qualities of prior knowledge it is already identified that all students have a variation of their ideas and their conceptions change when the time passes.

FUTURE RESEARCH DIRECTIONSResearch needs to develop more ways to assess, identifying differences in the students´ knowledge. It is also important to continue designing instruments to measure prior knowledge inherent qualities in other domains. As well it is likely that the instrument could be applied multiple times with more students to confirm if the results showed on this study have consistency with future research. In order to continue progress research that will lead us to detect which of the inherent qualities of prior knowledge (correct, incomplete, misconception or absence) has a differential effect on the learning process. As it was said, there are no instruments that have the same structure of the inherent qualities test. It is necessary to develop methodological research to obtain valid and reliable information. Another line for future research is to use the instrument to improve students´ academic performance with the correct interventions for the different inherent qualities from prior knowledge

CONCLUSIONSThe purpose of this instrument was to assess the inherent qualities of students´ prior knowledge. Several studies have stressed the importance to detect the characteristics of prior knowledge but there hasn´t been any attempt to design an instrument that would help to differentiate among these qualities. Once that the characteristics of prior knowledge can be assessed, the attempt to determine which characteristic appear more frequently in students and what differential effect each has in the learning process could be the next step. Thus, the results of assessing the validity and reliability of such an instrument will help future research in this respect. Once we can determine the amount and quality of students prior knowledge on the subject, we can design instructional materials accordingly and after instruction has taken place we can design another instrument that is consistent with the form of the instrument used to assess prior knowledge but this time it would be with the purpose of assessing students learning. Negative, positive and no effects can be expected in this future research but it would

be interesting to determine in what proportion the learning process will be influenced by each of these inherent qualities of prior knowledge. We will continue making efforts to understand the students’ learning process and trying to improve this process.

REFERENCESAlexander, P., & Judy, J. (1988). The Interaction of Domain-Specific and Strategic Knowledge in

Academic Performance. Review of Educational Research, 58(4), 375-404.Alexander, P., Shallert, D., & Hare, V. (1991). Coming to terms: How researchers in learning and

literacy talk about knowledge. Review of Educational Research, 61(3), 315–343.Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010).  How learning

works: Seven research-based principles for smart teaching. John Wiley & Sons.Arellano, L., Mendoza, G., & Villarreal, A. (2015). Medición del Conocimiento Previo y sus Variantes

que Influyen en el Aprendizaje. Academia Journals. Juárez, Chihuahua.Arroyo, Morales, Silva, Camacho, Canales & Carpio, (2008). Análisis funcional del conocimiento

previo: sus efectos sobre el ajuste lector. Acta Colombiana de Psicología, 11(2), 55-64.Ausubel, D., Novak, J., & Hanesian, H. (2006). Psicología Educativa: un punto de vista cognitivo.

México, D.F.: Trillas.Bandura, A. (1971). Social Learning Theory. General Learning Press.Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.Bandura, A. (2006). Guide for constructing self-efficacy scales. Self-efficacy beliefs of

adolescents, 5(307-337).Barniol, P., & Zavala, G. (2013). Testing Students’ Understanding of Vector Concepts. Presented in:

the Physics Education Research Conference 2013, Portland, Oregon.Barniol, P., & Zavala, G. (2014). Evaluación del entendimiento de los estudiantes en la representación

vectorial utilizando un test con opciones múltiples en español. Revista Mexicana de Física, 60(2014), 86-102.

Boiché, J., Sarrazin, P., Grouzet, F., Pelletier, L., & Chanal, J. (2008). Students’ motivational profiles and outcomes in physical education: A self-determination perspective. Journal of Educational Psychology, 100, 688-701.

Caramazza, A., McCloskey, M., & Green, B. (1981). Naive beliefs in “sophisticated” subjects: Misconceptions about trajectories of objects. Cognition, 9(2), 117-123.

Carey, S. (1999). Knowledge acquisition: Enrichment or conceptual change. In E. Margolis y S. Laurence, Concepts: core readings (pp. 459-487). MIT Press.

Carreño, F. (2001). Instrumentos de medición del rendimiento escolar. Cursos básicos para formación de profesores. Área de sistematización de la enseñanza. México: Trillas.

Chávez, C., y Saade, A. (2010). Procedimientos básicos para el análisis de reactivos. Cuaderno técnico 8, CENEVAL.

Cheng, D. K., & Peake, E. M. (1998). Fundamentos de electromagnetismo para ingeniería. Pearson Educación.

Chi, M. T. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. International handbook of research on conceptual change, 61-82.

Chi, M. T., Roscoe, R. D., Slotta, J. D., Roy, M., & Chase, C. C. (2012). Misconceived causal explanations for emergent processes. Cognitive Science, 36(1), 1-61.

Christen, W., & Murphy, T. (1991). Increasing comprehension by activating prior knowledge. ERIC Digest.

Coleman, J. (1966). Equality of Educational Opportunity, Washington D.C., U.S. Office of Education.Dochy, F.J. R. C. (1991a). Effects of prior knowledge on study results and learning processes:

Theoretical approaches and empirical evidence. Dochy, F. J. R. C. (1991b). Mapping" Prior Knowledge" or" Expertise": A Tentative Outline. A

Conceptual Map of Prior Knowledge and a Model for Research into the Domain-Specific

Prior Knowledge State (PKS). The Netherlands: Open University, Centre for Educational Technology and Innovation.

Dochy, F. J. R. C., & Alexander, P. (1995). Mapping Prior Knowledge: A Framework for Discussion among Researchers. European Journal of Psychology of Education, 10(3), 225-242.

Dochy, F., Moerkerke, G., & Segers, M. (1999). The Effect of Prior Knowledge on Learning in Educational Practice: Studies Using Prior Knowledge State Assessment. Evaluation & Research in Education, 13(3), 114-131. DOI: 10.1080/09500799908666952

Duit, R. (1993). Research on students' conceptions-developments and trends. In The Proceedings of the Third International Seminar on Misconceptions and Educational Strategies in Science and Mathematics. Ithaca, NY: Misconceptions Trust.

Emeigh, K. (2008). Activating Prior Knowledge Powerful Comprehension. Fernández, M., Mena, L., & Riviere, J. (2010) Failure and dropping out of school in Spain. La Caixa

foundation, No.29.Furió, C., & Guisasola, J. (2001). La enseñanza del concepto ce" campo eléctrico" basada en un

modelo de apendizaje como investigación orientada. Enseñanza de las ciencias: revista de investigación y experiencias didácticas, 19(2), 319-334.

Gallardo, K. (2010). Guía para elaboración de reactivos de opción múltiple.Garris, R., Ahlers, R., & Driskell, J. E. (2002). Games, motivation, and learning: A research and

practice model. Simulation & gaming, 33(4), 441-467.Giglers, H. & de Jong, T. (2005). The relation between prior knowledge and students' collaborative

discovery learning processes. Journal of Research in Science Teaching, 42 (3), 264-282. González, D., Castañeda, S., & Maytorena, M. (2000). Estilos de aprendizaje y aprovechamiento en

ingresantes universitarios. Revista de psicología, 18(2), 199-225.Graham, S., & Weiner, B. (1996). Theories and principles of motivation. Handbook of educational

psychology, 4, 63-84.Greca, I. M., & Moreira, M. A. (1998). Modelos mentales y aprendizaje de física en electricidad y

magnetismo. Enseñanza de las Ciencias, 16, 289-303.Grouws & Cebulla, (2000). Improving student achievement in mathematics. International Academy of

Education. Retrieved from http://www.ibe.unesco.orgHammer, D. (1996). Misconceptions or p-prims: How may alternative perspectives of

cognitive structure influence instructional perceptions and intentions? The Journal of the Learning Sciences, 5(2), 97-127.

Hewson, M. G., & Hewson, P. W. (1983). Effect of instruction using students' prior knowledge and conceptual change strategies on science learning. Journal of Research in Science Teaching, 20(8), 731-743.

Hestenes, D., Wells, M. y Swackhamer, G. (1992). Force Concept Inventory. The Physics Teacher, 30 (3), 141-151.

Hailikari, T. (2009). Assessing University Students’ Prior Knowledge: Implications for Theory and Practice. University of Helsinki, Departament of Education Research Report.

Hattie, J. (2009). Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement . Routledge, Taylor & Francis Group, London and New York. 39-53.

Hernández, G. (2012). Paradigmas en Psicología de la Educación. Educador: Paidós.Hertwig, R., & Todd, P. (2003). More is not always better: the benefits of cognitive limits. In D.

Hardman & L. Macchi, The Psychology of Reasoning and Decision Making: a Handbook (pp. 213–31). Chichester: Wiley.

Ibáñez, C. (2007). Metodología para la planeación de la educación superior: una aproximación desde la psicología interconductual. México: Universidad de Sonora/PIFI / Mora-Cantúa Editores.

Levesque, J. (1981). The interaction with incomplete knowledge bases: a formal treatment. Proceedings of the 7th International Joint Conference on Artificial Intelligence. Vancouver, B.C., Aug., pp. 240-245.

López, B. G., Esteban, P. R. G., & Pérez, Y. C. P. (2006). Enfoques de Aprendizaje y Rendimiento Académico en Estudiantes Universitarios. Bordón, 58(3), 45.

Karavel, J., & Hasley, A. (1977). Power and Ideology in Education. New York: Oxford University Press.

Kleres, J. (2010). Emotions and narrative analysis: A methodological approach. Journal for the Theory of Social Behavior, 41(2), 182-202.

Knight, R. D. (1995). The vector knowledge of beginning physics students. Physics Teacher, 33, 74-74.

Kerlinger, F. N., & Lee, H. B. (2002). Investigación del comportamiento. México DF, México: McGraw-Hill.

Marzano, R. (2004). Building background knowledge for academic achievement: research on what works in schools. Alexandria, VA USA: ASCD.

McKenzie, K., & Schweitzer, R. (2001). Who succeeds at university? Factors predicting academic performance in first year Australian university students. Higher education research and development, 20(1), 21-33.

Meltzer, D. (2002). The relationship between mathematics preparation and conceptual learning gains in physics: A possible ‘‘hidden variable’’ in diagnostic pretest scores. American Association of Physics Teachers. DOI: 10.1119/1.1514215.

Moss R, Schneider SH (2000). Uncertainties in the IPCC TAR: recommendations to lead authors for more consistent assessment and reporting. In R. Pachauri, T. Taniguchi, K. Tanaka (eds), Guidance papers on the cross cutting issues of the third assessment report of the IPCC. Technical report (pp. 33–51). World Meteorological Organization.

Nguyen, N. L., & Meltzer, D. E. (2003). Initial understanding of vector concepts among students in introductory physics courses. American journal of physics, 71(6), 630-638.

Oppenheimer, A. (2010). ¡Basta de Historias! La obsesión latinoamericana con el pasado y las 12 claves del futuro. Bogotá, Colombia: Nomos impresores.

Palacios, R., & Andrade, P. (2007). Desempeño académico y conductas de riesgo en adolescentes. Revista de educación y desarrollo, 7, 5.

Pine, K., Messer, D., & St. John, K. (2001). Children's misconceptions in primary science: a survey of teachers' views. Research in Science & Technological Education, 19(1), 79-96.

Porter, L. (2008). Imaginación y Educación: Complejidad y lentitud en el aprendizaje del diseño. Ciudad Juárez, Chihuahua: Universidad Autónoma de Ciudad Juárez.

Reber, A. S. (1989). Implicit learning and tacit knowledge. Journal of experimental psychology: General, 118(3), 219.

Rebich, S. & Gautier, C. (2005). Concept Mapping to Reveal Prior Knowledge. Journal of Geoscience Education, 53(4), 355-365.

Risbey, J. S., & Kandlikar, M. (2007). Expressions of likelihood and confidence in the IPCC uncertainty assessment process. Climatic change, 85(1-2), 19-31.

Risbey, J. S., & O’Kane, T. J. (2011). Sources of knowledge and ignorance in climate research. Climatic Change, 108(4), 755-773.

Roschelle, J. (1995). Learning in interactive environments: Prior knowledge and new experience. In J.H. Falk & L.D. Dierking, Public institutions for personal learning: Establishing a research agenda (pp. 37-51). Washington, DC: American Association of Museums.

Roschelle, J. (2004). Learning in interactive environments: Prior knowledge and new experience. Dartmouth: University of Massachusetts.

Sagastizabal, M., Perlo, C., Pivetta, B., & San Martín, P. (2009). Aprender a enseñar en contextos complejos: Multuculturalidad, diversidad y fragmentación. Buenos Aires-México: Noveduc.

Shapiro, A. M. (2004). How including prior knowledge as a subject variable may change outcomes of learning research. American Educational Research Journal, 41(1), 159-189.

Smith, J., DiSessa, P., & Roschelle, J. (1993). Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition. The Journal of the Learning Sciences, 3(2), 1994.

Smithson, M., Bartos, T., & Takemura, K. (2000). Human judgment under sample space ignorance. Risk Decision and Policy, 5(02), 135-150.

Stinebrickner, T., & Stinebrickner, R. (2013). Academic performance and college dropout: Using longitudinal expectations data to estimate a learning model (No. w18945). National Bureau of Economic Research.

Taber, K. S. (2001). Building the structural concepts of chemistry: Some considerations from educational research. Chemistry Education Research and Practice, 2(2), 123-158.

Tan, K. C. D., Goh, N. K., Chia, L. S., & Treagust, D. F. (2002). Development and application of a two‐tier multiple choice diagnostic instrument to assess high school students' understanding of inorganic chemistry qualitative analysis. Journal of research in Science Teaching, 39(4), 283-301.

Taylor, A. K., & Kowalski, P. (2004). Naive psychological science: The prevalence, strength, and sources of misconceptions. The Psychological Record, 54(1), 2.

Tinto, V. (1992). El abandono de los estudios superiores: una nueva perspectiva de las causas de abandono y su tratamiento. Cuadernos de planeación universitaria, 6(2). Asociación Nacional de Universidades e Instituciones de Educación Superior (ANUIES) Spanish versión from: Leaving College. Rethinking the causes and cures of student attrition. University the Chicago press. Chicago, Illinois.

Thompson, R., & Zamboanga, B. (2004). Academic Aptitude and Prior Knowledge as Predictors of Student Achievement in Introduction to Psychology. Journal of Educational Psychology, 96(4), 778–784. DOI: 10.1037/0022-0663.96.4.778.

Ungar, S. (2000). Knowledge, ignorance and the popular culture: climate change versus the ozone hole. Public Understanding of Science, 9(3), 297-312.

Wong, K. H. (2012). Implementation of problem-based learning in junior secondary science curriculum. Doctoral dissertation, The University of Hong Kong.

Zavala, G., Barniol, P., Singh, C., Sabella, M., & Rebello, S. (2010, October). Students' understanding of the concepts of vector components and vector products. In AIP Conference Proceedings (Vol. 1289, No. 1, p. 341).

Zemansky, S., Young, H. D., & Sears, F. W. (2005). Física universitaria (Vol. 2). Ed. Addison-Wesley Iberoamericana.

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of educational psychology, 81(3), 329.