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Name:
PITLIK, László

University:
Eötvös Loránd University, Faculty of Science
Pázmány Péter 1/A,
H-1017 Budapest, Hungary

Email:

Web:
ptlklszl.web.elte.hu

Next generation ICT in schools

Smartphones, interactive whiteboards, voting systems; e-learning, blended classroom and so on – in the field of education, ICT became more and more important lately. Probably, most teachers and researchers can agree that these tools and techniques fundamentally changed or at least can change in the near future the school system. The change seems to be inevitable and the critics may even call nowadays school system and methods of the 20th century “an ancient institution that has outlived its usage” [7].

The would-be trial on court, quoted above from Prince Ea may seem radical but it is certainly describing the feelings of many students and even teachers well. In this article, I want to introduce a conception of blackbox-based learning [5] which probably could be a fitting answer for the challenge presented by the conflict between creativity and mugging up (e.g.,historical events, scientific formulas). The aim of this paper is the introduction of a more objective evaluation system, too. This evaluation method is based on similarity analyses [6] and it can be considered as an artificial intelligence because of its ability to create suspicions in the way that human evaluation works. Due to the massive computing power available even in our pockets, similarity analysis can be, however, a more precise tool in data-driven decision making than any human intuition.

Both the educational blackboxes and the similarity-based evaluation systems have massively computerized background and they represent a new way of using ICT in schools. Not only modern devices are used for conventional reasons but they bring some essentially new aspects into the classroom. I believe that these methods really are forerunners of a next generation of ICT.

First of all, look at our blackboxes. Scientific calculators became more and more common among Hungarian students and these devices are allowed to be used by the students at the final examination process of secondary schools. However, graphic calculators are strictly prohibited even in many university classes. And students often have their own smartphones and laptops with impressive but mostly unexploited computing power. There are many best practices and opportunities to use these devices in the classroom but the typical way they become involved is – in my opinion – misleading. Yes, I agree that it is important to create fancy content and use the devices to bring fun sometimes into the classroom but quizzes, searches and apps for learning foreign language vocabulary stand for a very narrow field of the possibilities and potential of informatics and IT devices.

Even in these applications, students use their devices as blackboxes because they (or the the teachers) does not really know or understand what exactly is going on even at the software-level – not to mention circuit boards. The question is almost inevitable: why do not we use them more frequently to actually solve mathematical or chemical problems we come across?! I want to become a chemistry teacher so I want to demonstrate this with the topic of stoichiometric calculations.

Mugging up the formulas (e.g.,M = m/n, pV = nRT) does not prove to be a creative activity but researchers found that this is very important to be successful at solving exercises and problems. Schmidt and Jignéus have found [8] that Swedish students are relatively successful with their own “logical” problem solving strategies when it is about simpler exercises but they tend to use complete algorithms learned in school when facing some more complex problems. Tóth and Sebestyén, however, have found [10] that less than 40% of the Hungarian students use a well-defined strategy (mole method or proportionality method) in solving complex stoichiometry problems and – despite they are quite successful with a 70% rate of correct solutions – their knowledge structure does not indicate that they have actually understand the essence of these strategies and not just use them as isolated algorithms (i.e., blackboxes).

There are many other possible problem solving strategies just like the LEGO method by Molnár and Molnár-Hamvas [3] or the pathway / tunneling method by McCalla and Smith [9]. One thing is common in all of these seemingly different methods: all of them provide an equally good start for creating a problem solving robot / automaton. A robot which main ability is to determine the formula(s) in which every variable is known except for one, calculate the value of new variables and iterate this process until the solution (the value of a specific variable designated at the beginning) is found.

There is a finite number of formulas connected to a specific topic (e.g.,titration, electrochemistry / electrolysis) so partial problem solving automata may be constructed even by students using basic tools like the Solver extension of MSExcel. These partial automata would be able to solve literally all the typical exercises of the secondary school final examination. In this case, the ability of solving these problems means that any student who understands the problem well enough can set the automaton to execute the calculations and provide the solution.

Teaching stoichiometry problems from this perspective gives students the possibility to understand their own problem solving solutions better through implementing those for automata. Because knowledge can be translated to source code – and everything else is art. No more, no less: different from knowledge. Having the above mentioned automata (created by other students or professional manufacturers, cf. present-day scientific calculators) also offers to the students the opportunity to solve more problems even faster, thereby having some first impressions of the job market, where fast and correct solutions (by any accessible means) are often valued more than great lexical knowledge.

I want to emphasize: the blackbox-concept does not mean that students will know less, it means they will have the opportunity to focus on different things. They will have to understand the problem and its chemical significance as well as they will have to evaluate the given results, compare and contrast them with the context and other similar problems. Focusing on the problem as a whole instead of getting overwhelmed by the exhaustive calculations can not be a downgrade under any consideration.

I believe that in a class where students can focus on real problems, there some really valuable work can be done – and those students deserve and (on the other hand) demand the fair and objective evaluation of their work as well. The summative evaluation takes place usually at the end of a specific learning period and its result summarizes a student’s performance and progress in this time period. The summative evaluation is traditionally connected with selection, so it is effective only when it is objective, valid and reliable [1]. Grading systems (in Hungary this generally means a numerical grading from 1 (lowest grade) to 5 (top grade) – instead of grading with letters) are, however, problematic in many ways.

Mid-term and end-of-year grades are determined by various weighting methods to calculate the appropriate averages of the students’ previous grades. These weighting methods can be righteous but must be inevitably subjective as well. It depends mostly on the teacher how exactly this weighting takes place – and, even with the best intentions, this is very far from being egalitarian or objective.

The method of the similarity analysis is called COCO – component-based object comparison for objectivity – and it is an artificial intelligence. The same way as human intuition processes the information, this method [4] ranks every object by its every attribute and finally a multi-aspect evaluation takes place where the main goal is to minimize the difference between the original and the estimated values of the objects. At the classic setting, the method is to determine whether cheap things are really inferior and expensive things are superior or not.

There is also a modified setting called COCO_Y0 where the prior values (Y) of all the objects are equal (between them difference is zero – which gives the Y0 indication). This setting fits the evaluation situation of schools. In other words, we postulate that “everybody (in the class) is otherwise the same” [2]. If (and only if!) this special null-hypothesis can not be verified with any set of estimator staircase-functions on the ranks of the objects’ attributes, then we can say that there are real differences between the evaluated objects, between the students. The revealed differences are valid as well if – with reverse attribute-ranking – the new differences are opposed to the originals (i.e., if someone have won the beauty contest, he or she has to lose any “ugliness contest”).

The power of the similarity analysis resides in its simplicity. There is a free COCO-tool available online [11] and everyone can create an own version using an equation solver program. However, the results provided by the similarity analysis are very close to the human intuition so this method can facilitate the creation of suspicions. This artificial intelligence lacks the genius of human intuition (this is why it is constantly developed by the latter) but it has much more computing power so it can trustworthily process large amounts of data (e.g., rank hundreds of students by dozens of attributes – which situation occurs every year at the university admission process).

Summa summarum: the school of future will probably rely more and more on ICT and there are some great possibilities to start this process here and now. The devices we use as blackboxes in our everyday life are able to do more work (especially calculations) for us – we just need to encourage our students to really utilize this power. I have to say that I am optimistic. I believe that the students of the future will have the opportunity to attend schools which focus more and more on real problems, which will support them to use every accessible tool wisely to create better and better answers, innovations. And in this future, I hope that both the students and the teachers will be committed to objectivity and data-driven decision making. This is something what we can start building today, too.

Literature

[1] Golnhofer, E. (2003). A pedagógiai értékelés. In: Falus, I. (ed.) Didaktika. Nemzeti Tankönyvkiadó. Budapest.

[2] Mérő, L. (2007). Mindenki másképp egyforma. Tercium. Budapest.

[3] Molnár, J., & Molnár-Hamvas, L. (2011). LEGO-Method – New Strategy for Chemistry Calculation. US-China Education Review B, 7., 891-908.

[4] Pitlik, L. (2014). My-X Team, an Innovative „Idea-Breeding-Farm”. Innoreg. Gödöllő.

[5] Pitlik, L. jun. (2016). Az informatika alkalmazási lehetőségei kémiai számítási feladatok megoldása során. Magyar Internetes Agrár / Alkalmazott Informatikai Újság, No.220

[6] Pitlik, L. jun. (2017). Hasonlóságelemzés a szummatív értékelésben. Magyar Internetes Agrár / Alkalmazott Informatikai Újság, No.222

[7] Prince Ea. (2016). I just sued the school system! Youtube-video
https://www.youtube.com/watch?v=dqTTojTija8 (2017.02.27.)

[8] Schmidt, H-J., & Jignéus, C. (2003). Students Strategies in Solving Algorithmic Stoichiometry Problems. Chemistry Education: Research and Practice, 4.(3.), 305-317.

[9] Smith, A. L. H.,& McCalla, J. (2004). Letters: Problem Solving with Pathways. J. Chem. Educ., 81.(6.), 803-804.

[10] Tóth, Z., & Sebestyén, A. (2009.). Relationship between Students’ Knowledge Structure and Problem-Solving Strategy in Stoichiometric Problems based on the Chemical Equation. Eurasian J. Phys. Chem. Educ., 1.(1.), 8-20.

[11] MIAÚ My-X FREE (free online COCO-tool)
http://miau.gau.hu/myx-free/coco/index.html (2017.01.10.)